Systems and methods for particle analysis

ABSTRACT

The present disclosure provides systems and methods for sorting a cell. The system may comprise a flow channel configured to transport a cell through the channel. The system may comprise an imaging device configured to capture an image of the cell from a plurality of different angles as the cell is transported through the flow channel. The system may comprise a processor configured to analyze the image using a deep learning algorithm to enable sorting of the cell.

CROSS-REFERENCE

This application is a continuation of International Application No.PCT/US19/46557, filed Aug. 14, 2019, which is a continuation-in-part ofU.S. patent application Ser. No. 16/194,269, filed Nov. 16, 2018, whichclaims the benefit of U.S. Provisional Patent Application No.62/764,965, filed Aug. 15, 2018, each of which is entirely incorporatedherein by reference.

BACKGROUND

Cell physical and morphological properties can be used to study celltype and cell state and to diagnose diseases. Cell shape is one of themarkers of cell cycle. Eukaryotic cells show physical changes in shapewhich can be cell-cycle dependent, such as a yeast cell undergoingbudding or fission. Shape is also an indicator of cell state and canbecome an indicator used for clinical diagnostics. Blood cell shape maychange due to many clinical conditions, diseases, and medications, suchas the changes in red blood cells' morphologies resulting from parasiticinfections. Other parameters such as features of cell membrane,nuclear-to-cytoplasm ratio, nuclear envelope morphology, and chromatinstructure can also be used to identify cell type and disease state. Inblood, for instance, different cell types are distinguished by factorssuch as cell size, cell shape, and nuclear shape.

Biologists and cytopathologists use cell size and morphology to identifycell type and diagnose disease. This is mainly done by some sort ofmicroscopic imaging and manual analysis of the images. As a result, theexisting methods are time consuming, subjective, qualitative, and proneto error. Cytopathologists, for instance, review slides prepared fromdifferent tissues using a light microscope and look for features thatresemble characteristics of disease. This process is time-consuming andthe results are subjective and may be impacted by factors such as theorientation of the stained cells, how the slide was prepared, and theexpertise of the cytopathologists. Although there have been recentefforts to automate the analysis of cytology smears, there are stillchallenges. One of the main problems with the analysis of the smears isthe existence of contaminant cells that are hard to avoid and make itdifficult to detect rare cells or specific feature characteristics ofdisease. Other issues are the angles of the stained or smeared cells,which can obscure essential information for identification of a celltype or state. As such, there remains a need for improved methods and/orsystems for cell analysis.

SUMMARY

In an aspect, the present disclosure provides a method of sorting acell, comprising: (a) transporting a cell through a flow channel; (b)capturing a plurality of images of the cell from a plurality ofdifferent angles as the cell is transported through the flow channel;and (c) analyzing the plurality of images using a deep learningalgorithm to sort the cell.

In some embodiments, the method further comprises rotating the cell asthe cell is being transported through the flow channel. In someembodiments, the method further comprises applying a velocity gradientacross the cell to rotate the cell. In some embodiments, the cell isflown in a first buffer at a first velocity, and wherein the applyingthe velocity gradient across the cell comprises co-flowing a secondbuffer at a second velocity. In some embodiments, an axis of therotation of the cell and an additional axis of migration of the cellalong the flow channel are different. In some embodiments, the axis ofthe rotation of the cell is perpendicular to the additional axis of themigration of the cell along the flow channel.

In some embodiments, the method further comprises focusing the cell intoa streamline at a height within the flow channel as the cell is beingtransported through the flow channel. In some embodiments, the focusingcomprises subjecting the cell under an inertial lift force, wherein theinertial lift force is characterized by a Reynolds number of greaterthan 1. In some embodiments, the inertial lift force is characterized bya Reynolds number of at least 20.

In some embodiments, the plurality of images is captured at a rate ofabout 10 frames per second to about 500,000 frames per second.

In some embodiments, the plurality of angles extends around the cell orover a portion of the cell.

In some embodiments, the plurality of images of the cell are capturedfrom (1) a top side of the cell, (2) a bottom side of the cell, (3) afront side of the cell, (4) a rear side of the cell, (5) a left side ofthe cell, or (6) a right side of the cell. In some embodiments, theplurality of images of the cell are captured from at least two sidesselected from the group consisting of: (1) a top side of the cell, (2) abottom side of the cell, (3) a front side of the cell, (4) a rear sideof the cell, (5) a left side of the cell, and (6) a right side of thecell.

In some embodiments, the method further comprises sorting the cell basedon the analyzed plurality of images, by directing the cell to a selectedchannel of a plurality of channels downstream of the flow channel. Insome embodiments, the plurality of channels excluding the selectedchannel are closed prior to directing the cell to the selected channel.In some embodiments, the plurality of channels excluding the selectedchannel are closed using pressure, an electric field, a magnetic field,or a combination thereof. In some embodiments, the method furthercomprises validating the sorting of the cell using a light. In someembodiments, the validating comprises determining information associatedwith the cell using blockage or scattering of the light. In someembodiments, the information associated with the cell comprises a size,shape, density, texture, or speed of the cell. In some embodiments, thevalidating comprises (i) providing at least two light spots on theselected channel by directing at least two lights towards the selectedchannel, and (ii) determining a travel time of the cell between the atleast two light sports, wherein the at least two light spots are spacedapart by about 10 micrometer to about 1,000 micrometer. In someembodiments, the light comprises a laser. In some embodiments, thesorting comprises (i) directing a first cell to a first channel of theplurality of channels and (ii) directing a second cell to a secondchannel of the plurality of channels, wherein the first cell and thesecond cell have or are suspected of having one or more differentfeatures.

In some embodiments, the method further comprises sorting a plurality ofcells at a rate of at least 10 cells per second, wherein the pluralityof cells comprises the cell.

In some embodiments, the method further comprises sorting a plurality ofcells comprising the cell using a classifier; and feeding data from thesorting back to the classifier in order to train the classifier forfuture sorting. In some embodiments, the classifier comprises a neuralnetwork. In some embodiments, the classifier is configured to performclassification of each of the plurality of cells, based onclassification probabilities corresponding to a plurality of analyzedplurality of images of the plurality of cells.

In some embodiments, the cell is from a biological sample of a subject,and wherein the method further comprises determining a presence or anabsence of a condition or an attribute in the subject based on theanalyzed plurality of images. In some embodiments, the biological sampleof the subject is selected from the group consisting of: blood, plasma,serum, urine, perilymph fluid, feces, saliva, semen, amniotic fluid,cerebrospinal fluid, bile, sweat, tears, sputum, synovial fluid, vomit,bone, heart, thymus, artery, blood vessel, lung, muscle, stomach,intestine, liver, pancreas, spleen, kidney, gall bladder, thyroid gland,adrenal gland, mammary gland, ovary, prostate gland, testicle, skin,adipose, eye, brain, infected tissue, diseased tissue, malignant tissue,calcified tissue, and healthy tissue. In some embodiments, the malignanttissue comprises tumor, sarcoma, leukemia, or a derivative thereof.

In some embodiments, the capturing comprises illuminating the cell witha strobe light with an exposure time of less than 1 millisecond, whereinthe strobe light comprises a wavelength selected from the groupconsisting of: 470 nanometer (nm), 530 nm, 625 nm, and 850 nm.

In some embodiments, the method further comprises (i) analyzing each ofthe plurality of images using an ensemble of deep learning algorithms toclassify the cell at each of the plurality of different images at eachof the plurality of different angles, and (ii) aggregating a pluralityof classifications of the cell from the analyzing to determine a finalclassification of the cell.

In some embodiments, the cell is from a maternal serum. In someembodiments, the method further comprises identifying a nucleated redblood cell (RBC), wherein presence of the nucleated RBC is indicative ofa fetal abnormal condition. In some embodiments, the fetal abnormalcondition is fetal aneuploidy.

In some embodiments, the method further comprises (i) predicting anarrival time of the cell at a sorting junction, and (ii) adjusting avelocity of the cell to modify the arrival time of the cell at thesorting junction if the predicted arrival time of the cell is (1) beforecompletion of step (c) or (2) before completion of actuating one or morevalves at the sorting junction. In some embodiments, the method furthercomprises determining a probability of the cell arriving at the sortingjunction at the predicted arrival time. In some embodiments, theadjusting the velocity of the cell comprises decreasing the velocity ofthe cell. In some embodiments, the velocity of the cell is adjusted todelay the arrival time of the cell at the sorting junction. In someembodiments, the method further comprises (iii) detecting an actualarrival time of the cell at the sorting junction, and (iv) modifying oneor more cell analysis algorithms and/or actuation signals of the one ormore valves based on a comparison of the predicted arrival time and theactual arrival time. In some embodiments, (iv) is performed in realtime.

In some embodiments, the method further comprises (i) providing apopulation of cells comprising the cell, (ii) analyzing a subpopulationof the population of cells for a first time to detect a target cell,(iii) if a number of the target cell in the subpopulation is above apredetermined threshold number, collect the subpopulation, and (iv)analyzing the subpopulation for a second time. In some embodiments, themethod further comprises, in (ii), capturing one or more images of eachcell of the subpopulation. In some embodiments, the method furthercomprises capturing a single image of each cell from a single angle. Insome embodiments, the predetermined threshold number is at least 1target cell. In some embodiments, the predetermined threshold number isat least 2 target cells. In some embodiments, the predeterminedthreshold number is at least 5 target cells. In some embodiments, thesubpopulation comprises at least about 10 cells. In some embodiments,the subpopulation comprises at least about 50 cells. In someembodiments, the subpopulation comprises at least about 100 cells. Insome embodiments, the method further comprises, in (iv), (1) capturing aplurality of images of each cell of the subpopulation, (2) analyzingeach of the plurality of images, (3) classifying each cell of thesubpopulation, and (4) sorting each cell of the subpopulation.

In some embodiments, the method further comprises (i) capturing one ormore images of at least one of the sorted cells, and (ii) analyzing theone or more images using the deep learning algorithm to classify the atleast one sorted cell. In some embodiments, the method further comprisesmodifying one or more cell analysis algorithms based on the analysis ofthe at least one sorted cell. In some embodiments, the method furthercomprises performing the modifying in real time.

In an aspect, the present disclosure provides a system for sorting acell, comprising: a flow channel configured to transport a cell throughthe flow channel; an imaging device configured to capture a plurality ofimages of the cell from a plurality of different angles as the cell istransported through the flow channel; and a processor configured toanalyze the plurality of images using a deep learning algorithm to sortthe cell.

In some embodiments, the system is further configured to rotate the cellas the cell is being transported through the flow channel. In someembodiments, the system is further configured to apply a velocitygradient across the cell to rotate the cell. In some embodiments, theflow channel is configured to transport the cell in a first buffer at afirst velocity through the flow channel, and wherein the system isfurther configured to co-flow a second buffer at a second velocitythrough the flow channel, thereby to apply the velocity gradient acrossthe cell. In some embodiments, an axis of the rotation of the cell andan additional axis of migration of the cell along the flow channel aredifferent. In some embodiments, the axis of the rotation of the cell isperpendicular to the additional axis of the migration of the cell alongthe flow channel.

In some embodiments, the system is further configured to focus the cellinto a streamline at a height within the flow channel as the cell isbeing transported through the flow channel. In some embodiments, thesystem is configured to subject the cell under an inertial lift force tofocus the cell, wherein the inertial lift force is characterized by aReynolds number of greater than 1. In some embodiments, the inertiallift force is characterized by a Reynolds number of at least 20.

In some embodiments, a width or a height of the flow channel isnon-uniform along an axis of the flow channel. In some embodiments, thewidth or the height of the flow channel gradually increases along adirection of the flow channel through which the cell is transported.

In some embodiments, the flow channel comprises walls that are formed tofocus the cell into a streamline.

In some embodiments, the plurality of images is captured at a rate ofabout 10 frames per second to about 500,000 frames per second.

In some embodiments, the plurality of angles extends around the cell orover a portion of the cell.

In some embodiments, the plurality of images of the cell are capturedfrom (1) a top side of the cell, (2) a bottom side of the cell, (3) afront side of the cell, (4) a rear side of the cell, (5) a left side ofthe cell, or (6) a right side of the cell. In some embodiments, theplurality of images of the cell are captured from at least two sidesselected from the group consisting of: (1) a top side of the cell, (2) abottom side of the cell, (3) a front side of the cell, (4) a rear sideof the cell, (5) a left side of the cell, and (6) a right side of thecell.

In some embodiments, each of the plurality of images comprises atwo-dimensional image or a three-dimensional image.

In some embodiments, the flow channel is configured to transport aplurality of cells through the flow channel, wherein the plurality ofcells comprises the cell, and wherein the imaging device is furtherconfigured to capture one or more images of the plurality of cells asthe plurality of cells are transported through the flow channel. In someembodiments, the imaging device is configured to capture a single imageof the plurality of cells. In some embodiments, the imaging device isconfigured to individually capture one or more images of each of theplurality of cells. In some embodiments, the imaging device isconfigured to capture the one or more images of the plurality of cellsfrom a plurality of different angles relative to each of the pluralityof cells.

In some embodiments, the flow channel branches into a plurality ofchannels downstream of the flow channel, and wherein the system isconfigured to sort the cell based on the analyzed plurality of images,by directing the cell to a selected channel of the plurality ofchannels. In some embodiments, one or more of the plurality of channelscomprises a valve configured to modulate flow through the one or more ofthe plurality of channels. In some embodiments, the valve is a pressurevalve, an electric field valve, or a magnetic field valve. In someembodiments, the plurality of channels excluding the selected channelare closed prior to directing the cell to the selected channel. In someembodiments, the system further comprises a validation module configuredto detect the cell after the cell has been sorted, and validate thesorting of the cell based on the detection. In some embodiments, thevalidation module is configured to provide light to the cell todetermine information associated with the cell, wherein the detection isbased on blockage of scattering of the light by the cell. In someembodiments, the information associated with the cell comprises a size,shape, density, texture, or speed of the cell. In some embodiments, thevalidation module is configured to (i) provide at least two light spotson the selected channel by directing at least two lights towards theselected channel, and (ii) determine a travel time of the cell betweenthe at least two light sports, wherein the at least two light spots arespaced apart by about 10 micrometer to about 1,000 micrometer. In someembodiments, the light comprises a laser. In some embodiments, thesystem is configured to sort the cell by (i) directing a first cell to afirst channel of the plurality of channels and (ii) directing a secondcell to a second channel of the plurality of channels, wherein the firstcell and the second cell have or are suspected of having one or moredifferent features.

In some embodiments, the processor is configured to sort a plurality ofcells at a rate of at least 10 cells per second, wherein the pluralityof cells comprises the cell.

In some embodiments, the processor is configured to (i) use a classifierto sort a plurality of cells comprising the cell, and (ii) feed datafrom the sorting back to the classifier in order to train the classifierfor future sorting. In some embodiments, the classifier comprises aneural network. In some embodiments, the classifier is configured toperform classification of each of the plurality of cells, based onclassification probabilities corresponding to a plurality of analyzedplurality of images of the plurality of cells.

In some embodiments, the cell is from a biological sample of a subject,and wherein the method further comprises determining a presence or anabsence of a condition or an attribute in the subject based on theanalyzed plurality of images. In some embodiments, the biological sampleof the subject is selected from the group consisting of: blood, plasma,serum, urine, perilymph fluid, feces, saliva, semen, amniotic fluid,cerebrospinal fluid, bile, sweat, tears, sputum, synovial fluid, vomit,bone, heart, thymus, artery, blood vessel, lung, muscle, stomach,intestine, liver, pancreas, spleen, kidney, gall bladder, thyroid gland,adrenal gland, mammary gland, ovary, prostate gland, testicle, skin,adipose, eye, brain, infected tissue, diseased tissue, malignant tissue,calcified tissue, and healthy tissue. In some embodiments, the malignanttissue comprises tumor, sarcoma, leukemia, or a derivative thereof.

In some embodiments, the imaging device is configured to capture theplurality of images by illuminating the cell with a strobe light with anexposure time of less than 1 millisecond, wherein the strobe lightcomprises a wavelength selected from the group consisting of: 470nanometer (nm), 530 nm, 625 nm, and 850 nm.

In some embodiments, the processor is further configured to (i) analyzeeach of the plurality of images using an ensemble of deep learningalgorithms to classify the cell at each of the plurality of differentimages at each of the plurality of different angles, and (ii) aggregatea plurality of classifications of the cell from the analyzing todetermine a final classification of the cell.

In some embodiments, the cell is from a maternal serum. In someembodiments, the processor is configured to, based on the analyzing,identify a nucleated red blood cell (RBC), wherein presence of thenucleated RBC is indicative of a fetal abnormal condition. In someembodiments, the fetal abnormal condition is fetal aneuploidy.

In some embodiments, the system is further configured to (i) predict anarrival time of the cell at a sorting junction, and (ii) adjust avelocity of the cell to modify the arrival time of the cell at thesorting junction if the predicted arrival time of the cell is (1) beforecompletion of the analyzing or (2) before completion of actuating one ormore valves at the sorting junction. In some embodiments, the system isfurther configured to determine a probability of the cell arriving atthe sorting junction at the predicted arrival time. In some embodiments,the adjusting the velocity of the cell comprises decreasing the velocityof the cell. In some embodiments, the velocity of the cell is adjustedto delay the arrival time of the cell at the sorting junction. In someembodiments, the system is further configured to (iii) detect an actualarrival time of the cell at the sorting junction, and (iv) modify one ormore cell analysis algorithms and/or actuation signals of the one ormore valves based on a comparison of the predicted arrival time and theactual arrival time. In some embodiments, (iv) is performed in realtime.

In some embodiments, the system is configured to further comprising (i)analyze a subpopulation of a population of cells for a first time todetect a target cell, (ii) if a number of the target cell in thesubpopulation is above a predetermined threshold number, collect thesubpopulation, and (iii) analyze the subpopulation for a second time. Insome embodiments, the imaging device is configured to capture one ormore images of each cell of the subpopulation. In some embodiments, theimaging device is configured to capture a single image of each cell froma single angle. In some embodiments, the predetermined threshold numberis at least 1 target cell. In some embodiments, the predeterminedthreshold number is at least 2 target cells. In some embodiments, thepredetermined threshold number is at least 5 target cells. In someembodiments, the subpopulation comprises at least about 10 cells. Insome embodiments, the subpopulation comprises at least about 50 cells.In some embodiments, the subpopulation comprises at least about 100cells. In some embodiments, the system is configured to analyze thesubpopulation for the second time by (1) capturing a plurality of imagesof each cell of the subpopulation, (2) analyzing each of the pluralityof images, (3) classifying each cell of the subpopulation, and (4)sorting each cell of the subpopulation.

In some embodiments, the system is further configured to (i) capture oneor more images of at least one of the sorted cells, and (ii) analyze theone or more images using the deep learning algorithm to classify the atleast one sorted cell. In some embodiments, the imaging device or anadditional imaging device is configured to capture the one or moreimages of the at least one of the sorted cells. In some embodiments, theprocessor or an additional processor is configured to analyze the one ormore images using a deep learning algorithm to classify the at least onesorted cell. In some embodiments, the system is further configured tomodify one or more cell analysis algorithms based on the analysis of theat least one sorted cell. In some embodiments, the modifying isperformed in real time.

Another aspect of the present disclosure provides a non-transitorycomputer readable medium comprising machine executable code that, uponexecution by one or more computer processors, implements any of themethods above or elsewhere herein.

Another aspect of the present disclosure provides a system comprisingone or more computer processors and computer memory coupled thereto. Thecomputer memory comprises machine executable code that, upon executionby the one or more computer processors, implements any of the methodsabove or elsewhere herein.

Additional aspects and advantages of the present disclosure will becomereadily apparent to those skilled in this art from the followingdetailed description, wherein only illustrative embodiments of thepresent disclosure are shown and described. As will be realized, thepresent disclosure is capable of other and different embodiments, andits several details are capable of modifications in various obviousrespects, all without departing from the disclosure. Accordingly, thedrawings and description are to be regarded as illustrative in nature,and not as restrictive.

INCORPORATION BY REFERENCE

All publications, patents, and patent applications, and NCBI accessionnumbers mentioned in this specification are herein incorporated byreference to the same extent as if each individual publication, patent,patent application, or NCBI accession number was specifically andindividually indicated to be incorporated by reference. To the extentpublications and patents, patent applications, or NCBI accession numbersincorporated by reference contradict the disclosure contained in thespecification, the specification is intended to supersede and/or takeprecedence over any such contradictory material.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of the disclosure are set forth with particularity inthe appended claims. A better understanding of the features andadvantages of the present disclosure will be obtained by reference tothe following detailed description that sets forth illustrativeembodiments, in which the principles of the disclosure are utilized, andthe accompanying drawings (also “Figure” and “FIG.” herein), of which:

FIG. 1A conceptually illustrates a classification and/or sorting systemin accordance with one embodiment of the disclosure.

FIG. 1B conceptually illustrates a microfluidic design of a flow cell inaccordance with one embodiment of the disclosure.

FIG. 2 conceptually illustrates an exemplary long flow channel withcontrolled length to ensure that the objects in the system arrive at thebifurcation exactly when the decision is made and valve actuation iscompleted.

FIG. 3 conceptually illustrates an exemplary three-branch channeldesign.

FIG. 4 conceptually illustrates a particle rotating as it moves throughthe channel.

FIG. 5 conceptually illustrates an out-of-focus cell and an in-focuscell using inertia-based z focusing.

FIGS. 6A through 6D conceptually illustrate non-limiting examples ofco-flow designs.

FIG. 7 conceptually illustrates the adaptive labeling framework.

FIG. 8 conceptually illustrates the modifications made to Resnet50,wherein early layers are elongated in exchange of shrinkage oflate-stage layers, in order to enhance it and improve its accuracy.

FIG. 9 conceptually illustrates the multi-view ensemble inferenceframework.

FIG. 10 conceptually illustrates the multi-view to multi-channel dataframework.

FIG. 11 conceptually illustrates a non-limiting sorting design.

FIG. 12 conceptually illustrates a non-limiting sorting design that usespiezo actuator.

FIG. 13 conceptually illustrates a non-limiting sorting design that usesmagnetic actuation.

FIG. 14A conceptually illustrates a technique wherein the two lasersfrom the system of the present disclosure are recombined with a closeproximity.

FIGS. 14B and 14C conceptually illustrates examples of focusing two beamfocusing system.

FIG. 15 conceptually illustrates a validation technique that comprises abackchannel design.

FIG. 16 shows a computer system that is programmed or otherwiseconfigured to implement methods provided herein.

DETAILED DESCRIPTION

While various embodiments of the disclosure have been shown anddescribed herein, it will be obvious to those skilled in the art thatsuch embodiments are provided by way of example only. Numerousvariations, changes, and substitutions may occur to those skilled in theart without departing from the disclosure. It should be understood thatvarious alternatives to the embodiments of the disclosure describedherein may be employed.

Unless defined otherwise, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which the present disclosure belongs. In case of conflict,the present application including the definitions will control. Also,unless otherwise required by context, singular terms shall includepluralities and plural terms shall include the singular.

Classification and/or Sorting Systems

In an aspect, the present disclosure describes a cell sorting system.The cell sorting system can comprise a flow channel configured totransport a cell through the channel. The cell sorting system cancomprise an imaging device configured to capture an image of the cellfrom a plurality of different angles as the cell is transported throughthe flow channel. The cell sorting system can comprise a processorconfigured to analyze the image using a deep learning algorithm toenable sorting of the cell. The cell sorting system can be a cellclassification system. In some cases, the flow channel can be configuredto transport a solvent (e.g., liquid, water, media, alcohol, etc.)without any cell. The cell sorting system can have one or moremechanisms (e.g., a motor) for moving the imaging device relative to thechannel. Such movement can be relative movement, and thus the movingpiece can be the imaging device, the channel, or both. The processor canbe further configured to control such relative movement.

In some embodiments, the disclosure provides a classification and/orsorting system as illustrated in FIG. 1A shows a schematic illustrationof the cell sorting system with a flow cell design (e.g., a microfluidicdesign), with further details illustrated in FIG. 1B. In operation, asample 102 is prepared and injected by a pump 104 (e.g., a syringe pump)into a flow cell 105, or flow-through device. In some embodiments, theflow cell 105 is a microfluidic device. Although FIG. 1A illustrates aclassification and/or sorting system utilizing a syringe pump, any of anumber of perfusion systems can be used such as (but not limited to)gravity feeds, peristalsis, or any of a number of pressure systems. Insome embodiments, the sample is prepared by fixation and staining. Insome examples, the sample comprises live cells. As can readily beappreciated, the specific manner in which the sample is prepared islargely dependent upon the requirements of a specific application.

In some embodiments, a cell suspension sample is prepared atconcentrations ranging between 1×10⁴-5×10⁶ cells/mL. In someembodiments, a cell suspension sample is prepared at concentrationsranging between 1×10⁴-5×10⁴ cells/mL. In some embodiments, a cellsuspension sample is prepared at concentrations ranging between5×10⁴-1×10⁵ cells/mL. In some embodiments, a cell suspension sample isprepared at concentrations ranging between 1×10⁵-5×10⁵ cells/mL. In someembodiments, a cell suspension sample is prepared at concentrationsranging between 5×10⁵-1×10⁶ cells/mL. In some embodiments, a cellsuspension sample is prepared at concentrations ranging between1×10⁶-5×10⁶ cells/mL. In some embodiments, a cell suspension sample isprepared at concentrations ranging between 5×10⁴-5×10⁵ cells/mL. In someembodiments, a cell suspension sample is prepared at concentrationsranging between 1×10⁴-5×10⁵ cells/mL. In some embodiments, a cellsuspension sample is prepared at concentrations ranging between5×10⁴-1×10⁶ cells/mL. In some embodiments, a cell suspension sample isprepared at concentrations ranging between 2×10⁵-5×10⁵ cells/mL. In someembodiments, a cell suspension sample is prepared at concentrationsranging between 3×10⁵-5×10⁵ cells/mL. In some embodiments, a cellsuspension sample is prepared at concentrations ranging between4×10⁵-5×10⁵ cells/mL. In some embodiments, a cell suspension sample isprepared at concentrations ranging between 1×10⁵-4×10⁵ cells/mL. In someembodiments, a cell suspension sample is prepared at concentrationsranging between 2×10⁵-4×10⁵ cells/mL. In some embodiments, a cellsuspension sample is prepared at concentrations ranging between3×10⁵-4×10⁵ cells/mL. In some embodiments, a cell suspension sample isprepared at concentrations ranging between 1×10⁵-3×10⁵ cells/mL. In someembodiments, a cell suspension sample is prepared at concentrationsranging between 2×10⁵-3×10⁵ cells/mL. In some embodiments, a cellsuspension sample is prepared at concentrations ranging between1×10⁵-2×10⁵ cells/mL.

In some embodiments, a cell suspension sample is prepared at aconcentration of about 1×10⁴ cells/mL, about 5×10⁴ cells/mL, about 1×10⁵cells/mL, about 2×10⁵ cells/mL, about 3×10⁵ cells/mL, about 4×10⁵cells/mL, about 5×10⁴ cells/mL, about 5×10⁵ cells/mL, about 1×10⁶cells/mL, or about 5×10⁶ cells/mL.

The specific concentration utilized in a given classification and/orsorting system typically depends upon the capabilities of the system.Cells may be fixed and stained with colored dyes (e.g., Papanicolaou andWright Giemsa methods). Classification and/or sorting systems inaccordance with various embodiments of the disclosure can operate withlive, fixed and/or Wright Giemsa-stained cells. Staining can helpincrease the contrast of nuclear organelles and improve classificationaccuracy. In some embodiments the cells in the sample are not labelledand/or stained. After preparation, the cell suspension sample can beinjected into the microfluidic device using a conduit such as (but notlimited to) tubing and a perfusion system such as (but not limited to) aflow unit.

Examples of the flow unit may be, but are not limited to, a syringepump, a vacuum pump, an actuator (e.g., linear, pneumatic, hydraulic,etc.), a compressor, or any other suitable device to exert pressure(positive, negative, alternating thereof, etc.) to a fluid that may ormay not comprise one or more particles (e.g., one or more cells to beclassified, sorted, and/or analyzed). The flow unit may be configured toraise, compress, move, and/or transfer fluid into or away from themicrofluidic channel. In some examples, the flow unit may be configuredto deliver positive pressure, alternating positive pressure and vacuumpressure, negative pressure, alternating negative pressure and vacuumpressure, and/or only vacuum pressure. The flow cell of the presentdisclosure may comprise at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or moreflow units. The flow cell may comprise at most 10, 9, 8, 7, 6, 5, 4, 3,2, or 1 flow unit.

Each flow unit may be in fluid communication with at least 1, 2, 3, 4,5, 6, 7, 8, 9, 10, or more sources of fluid. Each flow unit may be influid communication with at most 10, 9, 8, 7, 6, 5, 4, 3, 2, or 1 fluid.The fluid may contain the particles (e.g., cells). Alternatively, thefluid may be particle-free. The flow unit may be configured to maintain,increase, and/or decrease a flow velocity of the fluid within themicrofluidic channel of the flow unit. Thus, the flow unit may beconfigured to maintain, increase, and/or decrease a flow velocity (e.g.,downstream of the microfluidic channel) of the particles. The flow unitmay be configured to accelerate or decelerate a flow velocity of thefluid within the microfluidic channel of the flow unit, therebyaccelerating or decelerating a flow velocity of the particles.

The fluid may be liquid or gas (e.g., air, argon, nitrogen, etc.). Theliquid may be an aqueous solution (e.g., water, buffer, saline, etc.).Alternatively, the liquid may be oil. In some cases, only one or moreaqueous solutions may be directed through the microfluidic channels.Alternatively, only one or more oils may be directed through themicrofluidic channels. In another alternative, both aqueous solution(s)and oil(s) may be directed through the microfluidic channels. In someexamples, (i) the aqueous solution may form droplets (e.g., emulsionscontaining the particles) that are suspended in the oil, or (ii) the oilmay form droplets (e.g., emulsions containing the particles) that aresuspended in the aqueous solution.

In some embodiments, a syringe pump injects the sample at about 10μL/min. In some embodiments, a syringe pump injects the sample at about50 μL/min. In some embodiments, a syringe pump injects the sample atabout 100 μL/min. In some embodiments, a syringe pump injects the sampleat about 150 μL/min. In some embodiments, a syringe pump injects thesample at about 200 μL/min. In some embodiments, a syringe pump injectsthe sample at about 250 μL/min. In some embodiments, a syringe pumpinjects the sample at about 500 μL/min. In some embodiments, a syringepump injects the sample at about 10 μL/min to about 500 μL/min, forexample at about 10 μL/min to about 50 μL/min, about 10 μL/min to about100 μL/min, about 10 μL/min to about 150 μL/min, about 10 μL/min toabout 200 μL/min, about 10 μL/min to about 250 μL/min, about 10 μL/minto about 300 μL/min, about 10 μL/min to about 350 μL/min, about 10μL/min to about 400 μL/min, or about 10 μL/min to about 450 μL/min.

As can readily be appreciated, any perfusion system, including but notlimited to peristalsis systems and gravity feeds, appropriate to a givenclassification and/or sorting system can be utilized.

As noted above, the flow cell 105 can be implemented as a fluidic devicethat focuses cells from the sample into a single streamline that isimaged continuously. In the illustrated embodiment, the cell line isilluminated by a light source 106 (e.g., a lamp, such as an arc lamp)and an optical system 110 that directs light onto an imaging region 138of the flow cell 105. An objective lens system 112 magnifies the cellsby directing light toward the sensor of a high-speed camera system 114.

In some embodiments, a 10×, 20×, 40×, 60×, 80×, 100×, or 200× objectiveis used to magnify the cells. In some embodiments, a 10×, objective isused to magnify the cells. In some embodiments, a 20× objective is usedto magnify the cells. In some embodiments, a 40× objective is used tomagnify the cells. In some embodiments, a 60× objective is used tomagnify the cells. In some embodiments, a 80× objective is used tomagnify the cells. In some embodiments, a 100× objective is used tomagnify the cells. In some embodiments, a 200× objective is used tomagnify the cells. In some embodiments, a 10× to a 200× objective isused to magnify the cells, for example a 10×-20×, a 10×-40×, a 10×-60×,a 10×-80×, or a 10×-100× objective is used to magnify the cells.

As can readily be appreciated by a person having ordinary skill in theart, the specific magnification utilized can vary greatly and is largelydependent upon the requirements of a given imaging system and cell typesof interest.

In some embodiments, one or more imaging devices may be used to captureimages of the cell. In some aspects, the imaging device is a high-speedcamera. In some aspects, the imaging device is a high-speed camera witha micro-second exposure time. In some instances, the exposure time is 1millisecond. In some instances, the exposure time is between 1millisecond (ms) and 0.75 millisecond. In some instances, the exposuretime is between 1 ms and 0.50 ms. In some instances, the exposure timeis between 1 ms and 0.25 ms. In some instances, the exposure time isbetween 0.75 ms and 0.50 ms. In some instances, the exposure time isbetween 0.75 ms and 0.25 ms. In some instances, the exposure time isbetween 0.50 ms and 0.25 ms. In some instances, the exposure time isbetween 0.25 ms and 0.1 ms. In some instances, the exposure time isbetween 0.1 ms and 0.01 ms. In some instances, the exposure time isbetween 0.1 ms and 0.001 ms. In some instances, the exposure time isbetween 0.1 ms and 1 microsecond (μs). In some aspects, the exposuretime is between 1 μs and 0.1 μs. In some aspects, the exposure time isbetween 1 μs and 0.01 μs. In some aspects, the exposure time is between0.1 μs and 0.01 μs. In some aspects, the exposure time is between 1 μsand 0.001 μs. In some aspects, the exposure time is between 0.1 μs and0.001 μs. In some aspects, the exposure time is between 0.01 μs and0.001 μs.

In some cases, the flow cell 105 may comprise at least 1, 2, 3, 4, 5, 6,7, 8, 9, 10, or more imaging devices (e.g., the high-speed camera system114) on or adjacent to the imaging region 138. In some cases, the flowcell may comprise at most 10, 9, 8, 7, 6, 5, 4, 3, 2, or 1 imagingdevice on or adjacent to the imaging region 138. In some cases, the flowcell 105 may comprise a plurality of imaging devices. Each of theplurality of imaging devices may use light from a same light source.Alternatively, each of the plurality of imaging devices may use lightfrom different light sources. The plurality of imaging devices may beconfigured in parallel and/or in series with respect to one another. Theplurality of imaging devices may be configured on one or more sides(e.g., two adjacent sides or two opposite sides) of the flow cell 105.The plurality of imaging devices may be configured to view the imagingregion 138 along a same axis or different axes with respect to (i) alength of the flow cell 105 (e.g., a length of a straight channel of theflow cell 105) or (ii) a direction of migration of one or more particles(e.g., one or more cells) in the flow cell 105.

One or more imaging devices of the present disclosure may be stationarywhile imaging one or more cells, e.g., at the imaging region 138.Alternatively, one or more imaging devices may move with respect to theflow channel (e.g., along the length of the flow channel, towards and/oraway from the flow channel, tangentially about the circumference of theflow channel, etc.) while imaging the one or more cells. In someexamples, the one or more imaging devices may be operatively coupled toone or more actuators, such as, for example, a stepper actuator, linearactuator, hydraulic actuator, pneumatic actuator, electric actuator,magnetic actuator, and mechanical actuator (e.g., rack and pinion,chains, etc.).

In some cases, the flow cell 105 may comprise at least 1, 2, 3, 4, 5, 6,7, 8, 9, 10, or more imaging regions (e.g., the imaging region 138). Insome cases, the flow cell 105 may comprise at most 10, 9, 8, 7, 6, 5, 4,3, 2, or 1 imaging region. In some examples, the flow cell 105 maycomprise a plurality of imaging regions, and the plurality of imagingregions may be configured in parallel and/or in series with respect toeach another. The plurality of imaging regions may or may not be influid communication with each other. In an example, a first imagingregion and a second imaging region may be configured in parallel, suchthat a first fluid that passes through the first imaging region does notpass through a second imaging region. In another example, a firstimaging region and a second imaging region may be configured in series,such that a first fluid that passes through the first imaging regionalso passes through the second imaging region.

In some embodiments, image sequences from cells are recorded at rates ofabout 10 frames/sec to about 10,000,000 frames/sec. In some embodiments,image sequences from cells are recorded at rates of at least about 10frames/sec. In some embodiments, image sequences from cells are recordedat rates of at most about 10,000,000 frames/sec. In some embodiments,image sequences from cells are recorded at rates of about 10 frames/secto about 100 frames/sec, about 10 frames/sec to about 1,000 frames/sec,about 10 frames/sec to about 10,000 frames/sec, about 10 frames/sec toabout 100,000 frames/sec, about 10 frames/sec to about 1,000,000frames/sec, about 10 frames/sec to about 10,000,000 frames/sec, about100 frames/sec to about 1,000 frames/sec, about 100 frames/sec to about10,000 frames/sec, about 100 frames/sec to about 100,000 frames/sec,about 100 frames/sec to about 1,000,000 frames/sec, about 100 frames/secto about 10,000,000 frames/sec, about 1,000 frames/sec to about 10,000frames/sec, about 1,000 frames/sec to about 100,000 frames/sec, about1,000 frames/sec to about 1,000,000 frames/sec, about 1,000 frames/secto about 10,000,000 frames/sec, about 10,000 frames/sec to about 100,000frames/sec, about 10,000 frames/sec to about 1,000,000 frames/sec, about10,000 frames/sec to about 10,000,000 frames/sec, about 100,000frames/sec to about 1,000,000 frames/sec, about 100,000 frames/sec toabout 10,000,000 frames/sec, or about 1,000,000 frames/sec to about10,000,000 frames/sec. In some embodiments, image sequences from cellsare recorded at rates of about 10 frames/sec, about 100 frames/sec,about 1,000 frames/sec, about 10,000 frames/sec, about 100,000frames/sec, about 1,000,000 frames/sec, or about 10,000,000 frames/sec.

In some embodiments, image sequences from cells are recorded at rates ofbetween 10,000-10,000,000 frames/sec using a high-speed camera, whichmay be color, monochrome, and/or imaged using any of a variety ofimaging modalities including (but not limited to) the near-infraredspectrum. In some embodiments, image sequences from cells are recordedat rates of between 50,000-5,000,000 frames/sec. In some embodiments,image sequences from cells are recorded at rates of between50,000-100,000 frames/sec. In some embodiments, image sequences fromcells are recorded at rates of between 100,000-1,000,000 frames/sec. Insome embodiments, image sequences from cells are recorded at rates ofbetween 100,000-500,000 frames/sec. In some embodiments, image sequencesfrom cells are recorded at rates of between 500,000-1,000,000frames/sec. In some embodiments, image sequences from cells are recordedat rates of between 1,000,000-5,000,000 frames/sec.

In some embodiments, image sequences from cells are recorded at a rateof about 50,000 frames/sec, about 100,000 frames/sec, about 200,000frames/sec, about 300,000 frames/sec, about 400,000 frames/sec, about500,000 frames/sec, about 750,000 frames/sec, about 1,000,000frames/sec, about 2,500,000 frames/sec, about 5,000,000 frames/sec, orabout 10,000,000 frames/sec.

In some embodiments, the imaging device used in the present disclosureis an ultra-high speed camera, wherein images are recorded at a rate ofup to 25,000,000 frames/sec. In some instances, the ultra-high speedcamera runs at 20,000 revolutions per second. In some instances, thehigh-speed camera has a resolution of 616×920 pixels.

The imaging device(s) (e.g., the high-speed camera) of the imagingsystem can comprise an electromagnetic radiation sensor (e.g., IRsensor, color sensor, etc.) that detects at least a portion of theelectromagnetic radiation that is reflected by and/or transmitted fromthe flow cell or any content (e.g., the cell) in the flow cell. Theimaging device can be in operative communication with one or moresources (e.g., at least 1, 2, 3, 4, 5, or more) of the electromagneticradiation. The electromagnetic radiation can comprise one or morewavelengths from the electromagnetic spectrum including, but not limitedto x-rays (about 0.1 nanometers (nm) to about 10.0 nm; or about 10¹⁸Hertz (Hz) to about 10¹⁶ Hz), ultraviolet (UV) rays (about 10.0 nm toabout 380 nm; or about 8×10¹⁶ Hz to about 10¹⁵ Hz), visible light (about380 nm to about 750 nm; or about 8×10¹⁴ Hz to about 4×10¹⁴ Hz), infrared(IR) light (about 750 nm to about 0.1 centimeters (cm); or about 4×10¹⁴Hz to about 5×10¹¹ Hz), and microwaves (about 0.1 cm to about 100 cm; orabout 10⁸ Hz to about 5×10¹¹ Hz). In some cases, the source(s) of theelectromagnetic radiation can be ambient light, and thus the cellsorting system may not have an additional source of the electromagneticradiation.

The imaging device(s) can be configured to take a two-dimensional image(e.g., one or more pixels) of the cell and/or a three-dimensional image(e.g., one or more voxels) of the cell.

In some embodiment, the imaging area is illuminated with a high-powerlight-emitting diode (LED) with exposure times that is less than 1millisecond (msec) to help prevent motion blurring of cells. In someembodiment, the imaging area is illuminated with a high-power LED withexposure times that is less than 1 microsecond (μsec) to help preventmotion blurring of cells. In some embodiments the imaging devicecomprises a combination of a strobe light and a camera. Strobe light,strobe, stroboscopic lamp, and strobing light may be usedinterchangeably. In some instances, a strobe light is a device used toproduce regular flashes of light. In some embodiments, a high-speedstrobe light is used in combination with one or more cameras. In someinstances, the high-speed strobe lights are capable of up to 2500strobes per second. In some instances, the high-speed strobe lights arecapable of up to 5000 strobes per second. In some instances, thehigh-speed strobe lights have a storage of electrical energy to pulsethe LEDs wherein the energy can go up to 2000 watts when the LEDs areactive. In some instances, the high-speed strobe light pulses the LEDwith up to 180 amps of DC current. In some instances, the strobe lightis white. In some instances, the strobe light is blue with a wavelengthof 470 nm. In some instances, the strobe light is green with awavelength of 530 nm. In some instances, the strobe light is red with awavelength of 625 nm. In some instances, the strobe light is infraredwith a wavelength of 850 nm. In some embodiments, the imaging devicecomprises a combination of a strobe light and one or more cameraswherein the cameras are high-speed camera. In some embodiments, theimaging device comprises a combination of a high-speed strobe light andone or more cameras, wherein the cameras are high-speed cameras.

As can readily be appreciated, the exposure times can differ acrossdifferent systems and can largely be dependent upon the requirements ofa given application or the limitations of a given system such as but notlimited to flow rates. Images are acquired and can be analyzed using animage analysis algorithm.

In some embodiments, the images are acquired and analyzed post-capture.In some aspects, the images are acquired and analyzed in real-timecontinuously. Using object tracking software, single cells can bedetected and tracked while in the field of view of the camera.Background subtraction can then be performed. In a number ofembodiments, the flow cell 106 causes the cells to rotate as they areimaged and multiple images of each cell are provided to a computingsystem 116 for analysis. In some embodiments, the multiple imagescomprise images from a plurality of cell angles.

The term “real time” or “real-time,” as used interchangeably herein,generally refers to an event (e.g., an operation, a process, a method, atechnique, a computation, a calculation, an analysis, an optimization,etc.) that is performed using recently obtained (e.g., collected orreceived) data. Examples of the event may include, but are not limitedto, analysis of a one or more images of a cell to classify the cell,updating one or more deep learning algorithms (e.g., neural networks)for classification and sorting, controlling actuation of one or morevalves by at a sorting bifurcation, etc. In some cases, a real timeevent may be performed almost immediately or within a short enough timespan, such as within at least 0.0001 ms, 0.0005 ms, 0.001 ms, 0.005 ms,0.01 ms, 0.05 ms, 0.1 ms, 0.5 ms, 1 ms, 5 ms, 0.01 seconds, 0.05seconds, 0.1 seconds, 0.5 seconds, 1 second, or more. In some cases, areal time event may be performed almost immediately or within a shortenough time span, such as within at most 1 second, 0.5 seconds, 0.1seconds, 0.05 seconds, 0.01 seconds, 5 ms, 1 ms, 0.5 ms, 0.1 ms, 0.05ms, 0.01 ms, 0.005 ms, 0.001 ms, 0.0005 ms, 0.0001 ms, or less.

The flow rate and channel dimensions can be determined to obtainmultiple images of the same cell from a plurality of different angles(i.e., a plurality of cell angles). A degree of rotation between anangle to the next angle may be uniform or non-uniform. In some examples,a full 360° view of the cell is captured. In some embodiments, 4 imagesare provided in which the cell rotates 90° between successive frames. Insome embodiments, 8 images are provided in which the cell rotates 45°between successive frames. In some embodiments, 24 images are providedin which the cell rotates 15° between successive frames. In someembodiments, at least three or more images are provided in which thecell rotates at a first angle between a first frame and a second frame,and the cell rotates at a second angle between the second frame and athird frame, wherein the first and second angles are different. In someexamples, less than the full 360° view of the cell may be captured, anda resulting plurality of images of the same cell may be sufficient toclassify the cell (e.g., determine a specific type of the cell).

The cell can have a plurality of sides. The plurality of sides of thecell can be defined with respect to a direction of the transport (flow)of the cell through the channel. In some cases, the cell can comprise astop side, a bottom side that is opposite the top side, a front side(e.g., the side towards the direction of the flow of the cell), a rearside opposite the front side, a left side, and/or a right side oppositethe left side. In some cases, the image of the cell can comprise aplurality of images captured from the plurality of angles, wherein theplurality of images comprise: (1) an image captured from the top side ofthe cell, (2) an image captured from the bottom side of the cell, (3) animage captured from the front side of the cell, (4) an image capturedfrom the rear side of the cell, (5) an image captured from the left sideof the cell, and/or (6) an image captured from the right side of thecell.

In some embodiments, a two-dimensional “hologram” of a cell can begenerated by superimposing the multiple images of the individual cell.The “hologram” can be analyzed to automatically classify characteristicsof the cell based upon features including but not limited to themorphological features of the cell.

In some embodiments, 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 images arecaptured for each cell. In some embodiments, 5 or more images arecaptured for each cell. In some embodiments, from 5 to 10 images arecaptured for each cell. In some embodiments, 10 or more images arecaptured for each cell. In some embodiments, from 10 to 20 images arecaptured for each cell. In some embodiments, 20 or more images arecaptured for each cell. In some embodiments, from 20 to 50 images arecaptured for each cell. In some embodiments, 50 or more images arecaptured for each cell. In some embodiments, from 50 to 100 images arecaptured for each cell. In some embodiments, 100 or more images arecaptured for each cell. In some cases, at least 1, 2, 3, 4, 5, 6, 7, 8,9, 10, 15, 20, 30, 40, 50, or more images may be captured for each cellat a plurality of different angles. In some cases, at most 50, 40, 30,20, 15, 10, 9, 8, 7, 6, 5, 4, 3, or 2 images may be captured for eachcell at a plurality of different angles.

In some embodiments, the imaging device is moved so as to capturemultiple images of the cell from a plurality of angles. In some aspects,the images are captured at an angle between 0 and 90 degrees to thehorizontal axis. In some aspects, the images are captured at an anglebetween 90 and 180 degrees to the horizontal axis. In some aspects, theimages are captured at an angle between 180 and 270 degrees to thehorizontal axis. In some aspects, the images are captured at an anglebetween 270 and 360 degrees to the horizontal axis.

In some embodiments, multiple imaging devices (for e.g. multiplecameras) are used wherein each device captures an image of the cell froma specific cell angle. In some aspects, 2, 3, 4, 5, 6, 7, 8, 9, or 10cameras are used. In some aspects, more than 10 cameras are used,wherein each camera images the cell from a specific cell angle,

As can readily be appreciated, the number of images that are captured isdependent upon the requirements of a given application or thelimitations of a given system. In several embodiments, the flow cell hasdifferent regions to focus, order, and/or rotate cells. Although thefocusing regions, ordering regions, and cell rotating regions arediscussed as affecting the sample in a specific sequence, a personhaving ordinary skill in the art would appreciate that the variousregions can be arranged differently, where the focusing, ordering,and/or rotating of the cells in the sample can be performed in anyorder. Regions within a microfluidic device implemented in accordancewith an embodiment of the disclosure are illustrated in FIG. 1B. Flowcell 105 may include a filtration region 130 to prevent channel cloggingby aggregates/debris or dust particles. Cells pass through a focusingregion 132 that focuses the cells into a single streamline of cells thatare then spaced by an ordering region 134. In some embodiments, thefocusing region utilizes “inertial focusing” to form the singlestreamline of cells. In some embodiments, the focusing region utilizes‘hydrodynamic focusing” to focus the cells into the single streamline ofcells. Optionally, prior to imaging, rotation can be imparted upon thecells by a rotation region 136. The optionally spinning cells can thenpass through an imaging region 138 in which the cells are illuminatedfor imaging prior to exiting the flow cell. These various regions aredescribed and discussed in further detail below. In some cases, therotation region 136 may precede the imaging region 138. In some cases,the rotation region 136 may be a part (e.g., a beginning portion, amiddle portion, and/or an end portion with respect to a migration of acell within the flow cell) of the imaging region 138. In some cases, theimaging region 138 may be a part of the rotation region 136.

In some embodiments, a single cell is imaged in a field of view of theimaging device, e.g. camera. In some embodiments, multiple cells areimaged in the same field of view of the imaging device. In some aspects,1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 cells are imaged in the same field ofview of the imaging device. In some aspects, up to 100 cells are imagedin the same field of view of the imaging device. In some instances, 10to 100 cells are imaged in the field of view, for example, 10 to 20cells, 10 to 30 cells, 10 to 40 cells, 10 to 50 cells, 10 to 60 cells,10 to 80 cells, 10 to 90 cells, 20 to 30 cells, 20 to 40 cells, 20 to 50cells, 20 to 60 cells, 20 to 70 cells, 20 to 80 cells, 20 to 90 cells,30 to 40 cells, 40 to 50 cells, 40 to 60 cells, 40 to 70 cells, 40 to 80cells, 40 to 90 cells, 50 to 60 cells, 50 to 70 cells, 50 to 80 cells,50 to 90 cells, 60 to 70 cells, 60 to 80 cells, 60 to 90 cells, 70 to 80cells, 70 to 90 cells, 90 to 100 cells are imaged in the same field ofview of the imaging device.

In some cases, only a single cell may be allowed to be transportedacross a cross-section of the flow channel perpendicular to the axis ofthe flow channel. In some cases, a plurality of cells (e.g., at least 2,3, 4, 5, or more cells; at most 5, 4, 3, 2, or 1 cell) may be allowed tobe transported simultaneously across the cross-section of the flowchannel perpendicular to the axis of the flow channel. In such a case,the imaging device (or the processor operatively linked to the imagingdevice) may be configured to track each of the plurality of cells asthey are transported along the flow channel.

In some embodiments, the classification and/or sorting systems inaccordance with various embodiments of the disclosure eliminate thevariability involved in manual preparation of slides, which rely onexpertise of the operator. Furthermore, image segmentation can beavoided. The classification and/or sorting system allows for high flowrates and high-throughputs can be achieved.

In some embodiments, the classification and/or sorting system includesan imaging system that can capture images at a rate of at least 100cells/second and a computing system that can classify at a rate of atleast 100 cells/second. In some embodiments, the classification and/orsorting system includes an imaging system that can capture images at arate of at least 500 cells/second and a computing system that canclassify at a rate of at least 500 cells/second. In some embodiments,the classification and/or sorting system includes an imaging system thatcan capture images at a rate of at least 1000 cells/second and acomputing system that can classify at a rate of at least 1000cells/second. In some embodiments, the classification and/or sortingsystem includes an imaging system that can capture images at a rate ofat least 2000 cells/second and a computing system that can classify at arate of at least 2000 cells/second. In some embodiments, theclassification and/or sorting system includes an imaging system that cancapture images at a rate of at least 5000 cells/second and a computingsystem that can classify at a rate of at least 5000 cells/second. Insome embodiments, the classification and/or sorting system includes animaging system that can capture images at a rate of at least 10,000cells/second and a computing system that can classify at a rate of atleast 10,000 cells/second.

In some embodiments, the classification and/or sorting system includesan imaging system that can capture images at a rate that is equal up to100 cells/second and a computing system that can classify up to 100cells/second. In some embodiments, the classification and/or sortingsystem includes an imaging system that can capture images at a rate thatis equal up to 500 cells/second and a computing system that can classifyup to 500 cells/second. In some embodiments, the classification and/orsorting system includes an imaging system that can capture images at arate that is equal to up to 1000 cells/second and a computing systemthat can classify up to 1000 cells/second. In some embodiments, theclassification and/or sorting system includes an imaging system that cancapture images at a rate that is equal to up to 2000 cells/second and acomputing system that can classify up to 2000 cells/second. In someembodiments, the classification and/or sorting system includes animaging system that can capture images at a rate that is equal to up to5000 cells/second and a computing system that can classify up to 5000cells/second. In some embodiments, the classification and/or sortingsystem includes an imaging system that can capture images at a rate thatis equal to up to 10,000 cells/second and a computing system that canclassify up to 10,000 cells/second.

The imaging system can include, among other things, a camera, anobjective lens system and a light source. In a number of embodiments,flow cells similar to those described above can be fabricated usingstandard 2D microfluidic fabrication techniques, requiring minimalfabrication time and cost.

Although specific classification and/or sorting systems, flow cells, andmicrofluidic devices are described above with respect to FIGS. 1A and1B, classification and/or sorting systems can be implemented in any of avariety of ways appropriate to the requirements of specific applicationsin accordance with various embodiments of the disclosure. Specificelements of microfluidic devices that can be utilized in classificationand/or sorting systems in accordance with some embodiments of thedisclosure are discussed further below.

Microfluidic Device Fabrication

Microfluidic devices in accordance with several embodiments of thedisclosure can be fabricated using a variety of methods. In someembodiments, a combination of photolithography and mold casting is usedto fabricate a microfluidic device. Conventional photolithographytypically involves the use of photoresist and patterned light to createa mold containing a positive relief of the desired microfluidic patternon top of a substrate, typically a silicon wafer. Photoresist is aphoto-curable material that can be used in photolithography to createstructures with feature sizes on the order of micrometers (μm). Duringfabrication, the photoresist can be deposited onto a substrate. Thesubstrate can be spun to create a layer of photoresist with a targeteddesired height. The photoresist layer can then be exposed to light,typically UV light (depending on the type of photoresist), through apatterned mask to create a cured pattern of photoresist. The remaininguncured portions can be developed away, leaving behind a positive reliefmold that can be used to fabricate microfluidic devices.

From the mold, material can be cast to create a layer containing anegative relief pattern. Inlet and outlet holes can be formed atappropriate regions, and the device can then be bonded to a backing tocreate a flow-through device, or flow cell, with flow channels (e.g.,microfluidic channels). A cross-section of the flow channel can have awidth and a height. The cross-section may be perpendicular to an axis ofthe flow channel (e.g., a direction of the flow of the solvent with orwithout cells inside the flow channel). The width and the height of theflow channel can be perpendicular to each other. The width and/or theheight of the flow channel can be uniform or non-uniform along the axisof the flow channel. In some cases, the width or the height of the flowchannel can increase or decrease (e.g., gradually or rapidly) along adirection of the flow channel through which the cell is transported. Insome cases, the width or the height of the flow channel can increasealong a section of the flow channel, and decrease along a differentsection of the flow channel.

The width or the height of the cross-section of the flow channel can beabout 1 μm to about 500 μm. The width or the height of the cross-sectionof the flow channel can be at least about 1 μm, 2 μm, 3 μm, 4 μm, 5 μm,6 μm, 7 μm, 8 μm, 9 μm, 10 μm, 20 μm, 30 μm, 40 μm, 50 μm, 60 μm, 70 μm,80 μm, 90 μm, 100 μm, 200 μm, 300 μm, 400 μm, 500 μm, or more. The widthor the height of the cross-section of the flow channel can be at mostabout 500 μm, 400 μm, 300 μm, 200 μm, 100 μm, 90 μm, 0 μm, 0 μm, 60 μm,50 μm, 40 μm, 30 μm, 20 μm, 10 μm, 9 μm, 8 μm, 7 μm, 6 μm, 5 μm, 4 μm, 3μm, 2 μm, 1 μm, or less.

The width or the height of the channel can increase or decrease alongthe direction of the flow channel at about 0.01 percent per μm (%/μm) toabout 1000%/μm. The increase of decrease of the width or height of thechannel along the direction of the flow channel can be at least about0.01%/μm, 0.05%/μm, 0.1%/μm, 0.5%/μm, 1%/μm, 5%/μm, 10%/μm, 50%/μm,100%/μm, 500%/μm, 1000%/μm, or more. The increase of decrease of thewidth or height of the channel along the direction of the flow channelcan be at most about 1000%/μm, 500%/μm, 100%/μm, 50%/μm, 10%/μm, 5%/μm,1%/μm, 0.5%/μm, 0.1%/μm, 0.05%/μm, 0.01%/μm, or less.

One or more flow channels of the flow cell of the present disclosure mayhave various shapes and sizes. For example, at least a portion of theflow channel (e.g., the focusing region 132, the ordering region 134,the rotation region 136, the imaging region 138, connecting regiontherebetween, etc.) may have a cross-section that is circular,triangular, square, rectangular, pentagonal, hexagonal, or any partialshape or combination of shapes thereof.

In some embodiments, the system of the present disclosure comprisesstraight channels with rectangular or square cross-sections. In someaspects, the system of the present disclosure comprises straightchannels with round cross-sections. In some aspects, the systemcomprises straight channels with half-ellipsoid cross-sections. In someaspects, the system comprises spiral channels. In some aspects, thesystem comprises round channels with rectangular cross-sections. In someaspects, the system comprises round channels with rectangular channelswith round cross-sections. In some aspects, the system comprises roundchannels with half-ellipsoid cross-sections. In some aspects, the systemcomprises channels that are expanding and contracting in width withrectangular cross-sections. In some aspects, the system compriseschannels that are expanding and contracting in width with roundcross-sections. In some aspects, the system comprises channels that areexpanding and contracting in width with half-ellipsoid cross-sections.

In some embodiments utilizing a rotation section, a two-layerfabrication process can be used to orient the rotation section so thatimaging of the cells as they rotate will provide images of cells atdifferent angles with a more accurate representation of cellularfeatures.

As can be readily appreciated, the microfluidic device can be fabricatedusing a variety of materials as appropriate to the requirements of thegiven application. In imaging applications, the microfluidic device istypically made of an optically transparent material such as (but notlimited to) polydimethylsiloxane (PDMS). In some embodiments, themicrofluidic device is made of silicon. In some embodiments, themicrofluidic device is made of low-temperature cofired ceramic (LTCC).In some embodiments, the microfluidic device is made of thermosetpolyester (TPE). In some embodiments, the microfluidic device is made ofpolystyrene (PS). In some embodiments, the microfluidic device is madeof polycarbonate (PC). In some embodiments, the microfluidic device ismade of poly-methyl methacrylate (PMMA). In some embodiments, themicrofluidic device is made of poly(ethylene glycol) diacrylate (PEGDA).In some embodiments, the microfluidic device is made ofpolyfluoropolyether diol methacrylate (PFPE-DMA). In some embodiments,the microfluidic device is made of polyurethane (PU).

Although a specific method of microfluidic device fabrication isdiscussed, any of a variety of methods can be implemented to fabricate amicrofluidic device utilized in accordance with various embodiments ofthe disclosure as appropriate to the requirements of a givenapplication.

Microfluidic Filters

Microfluidic devices in accordance with several embodiments of thedisclosure can include one or more microfluidic filters at the inlets,or further down, of the microfluidic device to prevent channel clogging.In other embodiments, filtration can occur off device. The specificdimensions and patterns of the filters and the microfluidic channel canvary and are largely dependent upon the sizes of the cells of interestand the requirements of a given application. Any of a variety ofmicrofluidic filter systems can be implemented on microfluidic devicesutilized in accordance with various embodiments of the disclosure asappropriate to the requirements of a given flow application.

Focusing Regions

The flow channel can comprise one or more walls that are formed to focusone or more cells into a streamline. The flow channel can comprise afocusing region comprising the wall(s) to focus the cell(s) into thestreamline. Focusing regions on a microfluidic device can take adisorderly stream of cells and utilize a variety of forces (for e.g.inertial lift forces (wall effect and shear gradient forces) orhydrodynamic forces) to focus the cells within the flow into astreamline of cells. In some embodiments, the cells are focused in asingle streamline. In some examples, the cells are focused in multiplestreamlines, for example at least 2, at least 3, at least 4, at least 5,at least 6, at least 7, at least 8, at least 9, or at least 10streamlines.

The focusing region receives a flow of randomly arranged cells via anupstream section. The cells flow into a region of contracted andexpanded sections in which the randomly arranged cells are focused intoa single streamline of cells. The focusing is driven by the action ofinertial lift forces (wall effect and shear gradient forces) acting oncells at Reynolds numbers>1, where channel Reynolds number is defined asfollows: Re_(c)=ρU_(m)W/μ, where U_(m) is the maximum fluid velocity, ρis the fluid density, μ is the fluid viscosity, and W is the channeldimension. In some embodiments, Reynolds numbers around 20-30 can beused to focus particles from about 10 μm to about 20 In someembodiments, the Reynolds number is such that laminar flow occurs withinthe microfluidic channels. As can readily be appreciated, the specificchannel Reynolds number can vary and is largely determined by thecharacteristics of the cells for which the microfluidic device isdesigned, the dimensions of the microfluidic channels, and the flow ratecontrolled by the perfusion system. The Reynolds number of the flowchannel of the present disclosure may or may not depend on one or moredimensional parameters (e.g., radius, diameter, cross-sectional area,volume, etc.) of a particle (e.g., a cell) flowing through the flowchannel.

In some embodiments, the focusing region is formed with curvilinearwalls that form periodic patterns. In some embodiments, the patternsform a series of square expansions and contractions. In otherembodiments, the patterns are sinusoidal. In further embodiments, thesinusoidal patterns are skewed to form an asymmetric pattern. Thefocusing region can be effective in focusing cells over a wide range offlow rates. In the illustrated embodiment, an asymmetricalsinusoidal-like structure is used as opposed to square expansions andcontractions. This helps prevent the formation of secondary vortices andsecondary flows behind the particle flow stream. In this way, theillustrated structure allows for faster and more accurate focusing ofcells to a single lateral equilibrium position. Spiral and curvedchannels can also be used in an inertia regime; however, these cancomplicate the integration with other modules. Finally, straightchannels where channel width is greater than channel height can also beused for focusing cells onto single lateral position. However, in thiscase, since there will be more than one equilibrium position in thez-plane, imaging can become problematic, as the imaging focal plane ispreferably fixed. As can readily be appreciated, any of a variety ofstructures that provide a cross section that expands and contracts alongthe length of the microfluidic channel or are capable of focusing thecells can be utilized as appropriate to the requirements of specificapplications.

The cell sorting system can be configured to focus the cell at a widthand/or a height within the flow channel along an axis of the flowchannel. The cell can be focused to a center or off the center of thecross-section of the flow channel. The cell can be focused to a side(e.g., a wall) of the cross-section of the flow channel. A focusedposition of the cell within the cross-section of the channel may beuniform or non-uniform as the cell is transported through the channel.

While specific implementations of focusing regions within microfluidicchannels are described above, any of a variety of channel configurationsthat focus cells into a single streamline can be utilized as appropriateto the requirements of a specific application in accordance with variousembodiments of the disclosure.

Ordering Regions

Microfluidic channels can be designed to impose ordering upon a singlestreamline of cells formed by a focusing region in accordance withseveral embodiments of the disclosure. Microfluidic channels inaccordance with some embodiments of the disclosure include an orderingregion having pinching regions and curved channels. The ordering regionorders the cells and distances single cells from each other tofacilitate imaging. In some embodiments, ordering is achieved by formingthe microfluidic channel to apply inertial lift forces and Dean dragforces on the cells. Dean flow is the rotational flow caused by fluidinertia. The microfluidic channel can be formed to create secondaryflows that apply a Dean drag force proportional to the velocity of thesecondary flows. Dean drag force scales with about ρU_(m) ²αD_(h) ²/R,where ρ is the fluid density, Um is the maximum fluid velocity,

D_(h)=2WH/(W+H) is the channel hydraulic diameter, α is the particledimension, and R is the curvature radius. The force balance betweeninertial lift and Dean drag forces determines particle equilibriumposition.

Depending on the particle size, the relative interior and exterior radiiof curvature (R_(1in,out)) of the channel and channel height (H_(C)) ofthe microfluidic channel can be determined to reach equilibrium atdesired locations. Different combinations of curved and pinching regions(and their parameters) can be used to achieve desired distance betweenparticles. Channel width in the pinching region can be adjusted suchthat the cells will not be squeezed through the channels, causingpossible damage to the cell membrane (the cells can, however, beslightly deformed without touching the channel walls while travelingthrough the pinching regions). Additionally, the squeezing could causedebris/residues from cell membrane left on the channel walls, which willchange the properties of the channel. The ordering in the pinchingregions is driven by instantaneous change in channel fluidic resistanceupon arrival of a cell/particle. Since the channel width in this regionis close to cell/particle dimensions, when a cell arrives at thepinching region, the channel resistance increases. Since the wholesystem is pressure-regulated (constant pressure), this can cause aninstantaneous decrease in flow rate and therefore spacing of the cells.The length and width of pinching region can be adjusted to reach desiredspacing between cells. The curved channel structure can also help withfocusing cells to a single z position, facilitating imaging.

Different geometries, orders, and/or combinations can be used. In someembodiments, pinching regions can be placed downstream from the focusingchannels without the use of curved channels. Adding the curved channelshelps with more rapid and controlled ordering, as well as increasing thelikelihood that particles follow a single lateral position as theymigrate downstream. As can readily be appreciated, the specificconfiguration of an ordering region is largely determined based upon therequirements of a given application.

Cell Rotating Regions and Imaging Regions

Architecture of the microfluidic channels of the flow cell of thepresent disclosure may be controlled (e.g., modified, optimized, etc.)to modulate cell flow along the microfluidic channels. Examples of thecell flow may include (i) cell focusing (e.g., into a single streamline)and (ii) rotation of the one or more cells as the cell(s) are migrating(e.g., within the single streamline) down the length of the microfluidicchannels. In some embodiments, microfluidic channels can be configuredto impart rotation on ordered cells in accordance with a number ofembodiments of the disclosure. One or more cell rotation regions (e.g.,the cell rotation region 136) of microfluidic channels in accordancewith some embodiments of the disclosure use co-flow of a particle-freebuffer to induce cell rotation by using the co-flow to applydifferential velocity gradients across the cells. In some cases, a cellrotation region may introduce co-flow of at least 1, 2, 3, 4, 5, or morebuffers (e.g., particle-free, or containing one or more particles, suchas polymeric or magnetic particles) to impart rotation on one or morecells within the channel. In some cases, a cell rotation region mayintroduce co-flow of at most 5, 4, 3, 2, or 1 buffer to impart therotation of one or more cells within the channel. In some examples, theplurality of buffers may be co-flown at a same position along the lengthof the cell rotation region, or sequentially at different positionsalong the length of the cell rotation region. In some examples, theplurality of buffers may be the same or different. In severalembodiments, the cell rotation region of the microfluidic channel isfabricated using a two-layer fabrication process so that the axis ofrotation is perpendicular to the axis of cell downstream migration andparallel to cell lateral migration.

Cells may be imaged in at least a portion of the cell rotating region,while the cells are tumbling and/or rotating as they migrate downstream.Alternatively or in addition to, the cells may be imaged in an imagingregion that is adjacent to or downstream of the cell rotating region. Insome examples, the cells may be flowing in a single streamline within aflow channel, and the cells may be imaged as the cells are rotatingwithin the single streamline. A rotational speed of the cells may beconstant or varied along the length of the imaging region. This mayallow for the imaging of a cell at different angles (e.g., from aplurality of images of the cell taken from a plurality of angles due torotation of the cell), which may provide more accurate informationconcerning cellular features than can be captured in a single image or asequence of images of a cell that is not rotating to any significantextent. This also allow a 3D reconstruction of the cell using availablesoftware since the angles of rotation across the images are known.Alternatively, every single image of the sequence of image many beanalyzed individually to analyze (e.g., classify) the cell from eachimage. In some cases, results of the individual analysis of the sequenceof images may be aggregated to determine a final decision (e.g.,classification of the cell).

In some embodiments, a similar change in velocity gradient across thecell is achieved by providing a change in channel height (i.e., thedimension that is the smaller of the two dimensions of the cross sectionof the microfluidic channel and the dimension perpendicular to theimaging plane). This increase in channel height should be such that thewidth continues to be greater than the height of the channel. Also inthe case of increasing channel height, there can be a shift in cellfocusing position in the height dimension, which should be accounted forduring imaging and adjustment of the imaging focal plane. In an example,the channel height may be increased by (i) changing a bottom surface ofthe channel (e.g., depressing the bottom surface of the channel) and/or(ii) changing a top surface of the channel (e.g., raising the topsurface of the channel).

In some embodiments, a cell rotation region of a microfluidic channelincorporates an injected co-flow prior to an imaging region inaccordance with an embodiment of the disclosure. Co-flow may beintroduced in the z plane (perpendicular to the imaging plane) to spinthe cells. Since the imaging is done in the x-y plane, rotation of cellsaround an axis parallel to the y-axis provides additional information byrotating portions of the cell that may have been occluded in previousimages into view in each subsequent image. Due to a change in channeldimensions, at point xo, a velocity gradient is applied across thecells, which can cause the cells to spin. The angular velocity of thecells depends on channel and cell dimensions and the ratio between Q1(main channel flow rate) and Q2 (co-flow flow rate) and can beconfigured as appropriate to the requirements of a given application. Insome embodiments, a cell rotation region incorporates an increase in onedimension of the microfluidic channel to initiate a change in thevelocity gradient across a cell to impart rotation onto the cell. Insome aspects, a cell rotation region of a microfluidic channelincorporates an increase in the z-axis dimension of the cross section ofthe microfluidic channel prior to an imaging region in accordance withan embodiment of the disclosure. The change in channel height caninitiate a change in velocity gradient across the cell in the z axis ofthe microfluidic channel, which can cause the cells to rotate as withusing co-flow.

Although specific techniques for imparting velocity gradients upon cellsare described above, any of a variety of techniques can be utilized toimpart rotation on a single streamline of cells as appropriate to therequirements of specific applications in accordance with variousembodiments of the disclosure.

Flowing Cells

In some embodiments, the system and methods of the present disclosurefocuses the cells in microfluidic channels. The term focusing as usedherein broadly means controlling the trajectory of cell/cells movementand comprises controlling the position and/or speed at which the cellstravel within the microfluidic channels. In some embodiments controllingthe lateral position and/or the speed at which the particles travelinside the microfluidic channels, allows to accurately predict the timeof arrival of the cell at a bifurcation. The cells may then beaccurately sorted. The parameters critical to the focusing of cellswithin the microfluidic channels include, but are not limited to channelgeometry, particle size, overall system throughput, sampleconcentration, imaging throughput, size of field of view, and method ofsorting.

In some embodiments the focusing is achieved using inertial forces. Insome embodiments, the system and methods of the present disclosure focuscells to a certain height from the bottom of the channel using inertialfocusing (Dino Di Carlo, 2009, Lab on a Chip, which is incorporatedherein by reference in its entirety). In these embodiments, the distanceof the cells from the objective is equal and images of all the cellswill be clear. As such, cellular details, such as nuclear shape,structure, and size appear clearly in the outputted images with minimalblur. In some aspects, the system disclosed herein has an imagingfocusing plane that is adjustable. In some aspects, the focusing planeis adjusted by moving the objective or the stage. In some aspects, thebest focusing plane is found by recording videos at different planes andthe plane wherein the imaged cells have the highest Fourier magnitude,thus, the highest level of detail and highest resolution, is the bestplane (FIG. 5).

In some embodiments, the system and methods of the present disclosureutilize a hydrodynamic-based z focusing system to obtain a consistent zheight for the cells of interests that are to be imaged. In someaspects, the design comprises hydrodynamic focusing using multipleinlets for main flow and side flow. In some aspects, thehydrodynamic-based z focusing system is a triple-punch design (FIG. 6A).In some aspects, the design comprises hydrodynamic focusing with threeinlets, wherein the two side flows pinch cells at the center. Forcertain channel designs, dual z focus points may be created, wherein adouble-punch design similar to the triple-punch design may be used tosend objects to one of the two focus points to get consistent focusedimages. In some aspects, the design comprises hydrodynamic focusing with2 inlets, wherein only one side flow channel is used and cells arefocused near channel wall. In some aspects, the hydrodynamic focusingcomprises side flows that do not contain any cells and a middle inletthat contains cells. The ratio of the flow rate on the side channel tothe flow rate on the main channel determines the width of cell focusingregion. In some aspects, the design is a combination of the above. Inall aspects, the design is integrable with the bifurcation and sortingmechanisms disclosed herein. In some aspects, the hydrodynamic-based zfocusing system is used in conjunction with inertia-based z focusing.

FIG. 6B illustrates an example of a single side-flow system forhydrodynamic focusing. A top inlet may direct flow of a solution withone or more particles across a main channel (going across the schematicfrom left to right), and a side inlet (beneath the top inlet) may directa side flow to the main channel, thereby focusing the particle(s) near awall of the main channel. FIGS. 6C and 6D illustrates examples of doubleside-flow system for hydrodynamic focusing. A main inlet (a large inletwith a pentagonal shape) may direct flow of a solution with one or moreparticles across a main channel (going across the schematic from left toright), and two side channels may each direct a side flow to the mainchannel, thereby focusing the particle(s) towards the center of the mainchannel. In some cases, the two side channels may be in fluidcommunication with two separate side inlets, as illustrated in FIG. 6C.In some cases, the two side channels may be in fluid communication witha single, shared inlet, as illustrated in FIG. 6D.

In some embodiments, the terms “particles”, “objects”, and “cells” areused interchangeably. In some aspects, the cell is a live cell. In someaspects, the cell is a fixed cell (e.g., in methanol orparaformaldehyde). In some cases, one or more cells may be coupled(e.g., attached covalently or non-covalently) to a substrate (e.g., apolymeric bead or a magnetic bead) while flowing through the flow cell.In some cases, the cell(s) may not be coupled to any substrate whileflowing through the flow cell.

Method to Control Particle Arrival Times

In various embodiments, the systems and methods of the presentdisclosure sort the cells by ensuring that the cells in the systemarrive at the bifurcation exactly when the decision is made and valveactuation is completed. In some aspects, ensuring that the objectsarrive at the bifurcation exactly when (or prior to when) the decisionis made and the valve actuation is completed is achieved by controllinglength of the microfluidic channels. In some embodiments the channel isa long channel and the length of the channel is determined based onfactors that include, but are not limited to, (i) the estimated time ofdecision making, (ii) latency and opening window of switching mechanism,such as valves, and (iii) velocity of the particles (FIG. 2).

In some embodiments, ensuring that the objects arrive at the bifurcationexactly when (or prior to when) the decision is made and the valveactuation is completed is achieved by controlling the velocity of thecells. Controlling the velocity of the cells may comprise increasing thevelocity of the cells, decreasing the velocity of the cells, increasingacceleration of the cells, and/or decreasing acceleration of the cells.In some examples, this is achieved by branching the microfluidicchannels. In these examples one or more branches are used to guide thefluid into one or more of multiple channels, thereby modifying (e.g.,dropping) the velocity of fluid and particles in any one channel whilekeeping particles in focus. In some aspects, the factor by which thevelocity is modified (e.g., dropped) depends on parameters including butlimited to number of channels, relative width of channels, and/orrelative height of channels. An exemplary snapshot of a 3-branch designwith particles focused to center is shown in (FIG. 3). In someembodiments the microfluidic channel is divided into 2, 3, 4, 5, 6, 7,8, 9, or 10 branches.

In some embodiments, ensuring that the particles arrive at thebifurcation exactly when the decision is made and the valve actuation iscompleted is achieved by a gradual change (e.g., increase or decrease)in width and/or height of the channel. In some examples, the velocity ofthe particles can be decreased in a controlled manner by guiding theparticles into a wider and/or taller channel. In some examples, thevelocity of the particles can be increased in a controlled manner byguiding the particles into a wider and/or taller channel. In someembodiments, the angle and/or length of expansion are designed to avoidparticles from changing trajectory. This will prevent the particles fromgetting out of focus.

In some embodiments, ensuring that the particles arrive at thebifurcation exactly when (or prior to when) the decision is made and thevalve actuation is completed is achieved by designs within themicrofluidic channels. Such designs include but are not limited tocurved and/or spiral designs that can delay particles before they arriveat bifurcation, while keeping their lateral positions as well as theirrelative longitudinal position to each other constant and controlled.

In some embodiments, controlling the velocity of the particles whilethey are traveling (e.g., flowing) in the microfluidic channel may beachieved by using one or more flow units, e.g., syringe pumps, asprovided in the present disclosure.

In some embodiments, the length of the channel may be fixed (orconstant) during operation of the flow cell. Alternatively, the lengthof at least a portion of the channel may be modified (e.g., bystretching, shortening, or bending the at least the portion of thechannel), thereby to modify the arrival time of the cells at thebifurcation. The one or both ends and/or one or more sides of the atleast the portion of the channel may be operatively coupled to one ormore actuators to modify the length of the at least the portion of thechannel. Such modified portion of the channel may be disposed prior toor subsequent to the imaging region of the flow cell.

In some aspects, the methods and the systems disclosed herein ensurethat the particles arrive at the bifurcation exactly when (or prior towhen) the decision is made and valve actuation is completed, as well asensure that the lateral position of the particles upon their arrival iscontrolled.

In some aspects, the methods and the systems disclosed herein maycomprise determining that the predicted arrival time of a particle(e.g., a cell) at the bifurcation is later than a predetermined durationof time subsequent to (i) when the decision of cell classification ismade and (ii) when valve actuation is completed. In such cases, thevelocity of the cell's migration along the microfluidic channel andtowards the bifurcation may be increased to improve efficiency (e.g.,reduce time) of the classification and sorting, yet without negativelyaffecting efficiency and/or accuracy of the classification and sortingresults. In some cases, the predetermined duration of time may be atleast 0.00001 seconds, 0.00005 seconds, 0.0001 seconds, 0.0005 seconds,0.001 seconds, 0.005 seconds, 0.01 seconds, 0.05 seconds, 0.1 seconds,0.5 seconds, 1 seconds, 5 seconds, 10 seconds, or more. In some cases,the predetermined duration of time may be at most 10 seconds, 5 seconds,1 second, 0.5 seconds, 0.1 seconds, 0.05 seconds, 0.01 seconds, 0.005seconds, 0.001 seconds, 0.0005 seconds, 0.0001 seconds, 0.00005 seconds,0.00001 seconds, or less.

In some embodiments, the methods and the systems disclosed herein maycomprise determining predicted arrival times of a particle at aplurality of locations of the flow channel. In some embodiments, themethods and the systems disclosed herein may comprise determiningpredicted arrival times of a plurality of particles at the bifurcation.In some embodiments, the methods and the systems disclosed herein maycomprise determining predicted arrival times of a plurality of particlesat a plurality of bifurcations.

In some embodiments, predicting the arrival time of a particle at thebifurcation may comprise determining (e.g., calculating) a probability(i.e., likelihood) of the particle arriving at the bifurcation at thepredicted arrival time. The predicted arrival time may have aprobability (on a scale from 0 to 1.0) of at least, 0.5, 0.55, 0.6,0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, or more (e.g., 1.0). Thepredicted arrival time may have a probability (on a scale from 1.0 to 0)of at most 1, 0.95, 0.9, 0.85, 0.8, 0.75, 0.7, 0.65, 0.6, 0.55, 0.5, orless. In some embodiments, one or more algorithms for predicting thearrival time may be updated in real time (e.g., by comparing it to anactual arrival time that is measured), thereby to generate a predictedarrival time with a higher probability.

Imaging and Classification

A variety of techniques can be utilized to classify images of cellscaptured by classification and/or sorting systems in accordance withvarious embodiments of the disclosure. In some embodiments, the imagecaptures are saved for future analysis/classification either manually orby image analysis software. Any suitable image analysis software can beused for image analysis. In some embodiments, image analysis isperformed using OpenCV. In some embodiments, analysis and classificationis performed in real time. In some embodiments images are captured atframe rates between 10-10,000,000 frames per second, for example between10 frames/sec to about 100 frames/sec, about 10 frames/sec to about1,000 frames/sec, about 10 frames/sec to about 10,000 frames/sec, about10 frames/sec to about 100,000 frames/sec, about 10 frames/sec to about1,000,000 frames/sec, about 10 frames/sec to about 10,000,000frames/sec, about 100 frames/sec to about 1,000 frames/sec, about 100frames/sec to about 10,000 frames/sec, about 100 frames/sec to about100,000 frames/sec, about 100 frames/sec to about 1,000,000 frames/sec,about 100 frames/sec to about 10,000,000 frames/sec, about 1,000frames/sec to about 10,000 frames/sec, about 1,000 frames/sec to about100,000 frames/sec, about 1,000 frames/sec to about 1,000,000frames/sec, about 1,000 frames/sec to about 10,000,000 frames/sec, about10,000 frames/sec to about 100,000 frames/sec, about 10,000 frames/secto about 1,000,000 frames/sec, about 10,000 frames/sec to about10,000,000 frames/sec, about 100,000 frames/sec to about 1,000,000frames/sec, about 100,000 frames/sec to about 10,000,000 frames/sec, orabout 1,000,000 frames/sec to about 10,000,000 frames/sec andclassification is performed in real time. In some embodiments images arecaptured at frame rates between 100,000 and 500,000 frames per secondand classification is performed in real time. In some aspects, imagesare captured at frame rates between 200,000 and 500,000 frames persecond and classification is performed in real time. In some aspects,images are captured at frame rates between 300,000 and 500,000 framesper second and classification is performed in real time. In someaspects, images are captured at frame rates between 300,000 and 500,000frames per second and classification is performed in real time. In someaspects, images are captured at frame rates between 400,000 and 500,000frames per second and classification is performed in real time.

In some embodiments, the objects placed in the system disclosed hereinflow at very high speeds through the channels of the system in order tomaintain a high throughput and highly effective inertia focusing. Insome aspects, the system of the present disclosure comprises a veryhigh-speed camera with microsecond exposure time to decrease blur inoutputted images. In some aspects, the high-speed camera captures imagesat frame rates between 200,000 and 500,000 frames per second. In someaspects, the high-speed camera captures images at frame rates between300,000 and 500,000 frames per second. In some aspects, the high-speedcamera captures images at frame rates between 300,000 and 500,000 framesper second. In some aspects, the high-speed camera captures images atframe rates between 400,000 and 500,000 frames per second. In someaspects, the system comprises a high-speed strobing light. In someaspects, the system comprises a high-speed strobing light with a slowercamera, wherein multiple snapshots of the same object may be imaged ontoone image frame (e.g., a single image frame), which may be and separatedlater with the feature extraction and classification algorithm disclosedherein.

In some embodiments, the system and methods of the present disclosurecomprise collecting a plurality of images of objects in the flow. Insome aspects, the plurality of images comprises at least 20 images ofcells. In some aspects, the plurality of images comprises at least 19,18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, or 2 images ofcells. In some embodiments, the plurality of images comprises imagesfrom multiple cell angles. In some aspects, the plurality of images,comprising images from multiple cell angles, help derive extra featuresfrom the particle which would typically be hidden if the particle isimaged from a single point-of-view. In some aspects, without wishing tobe bound by theory, the plurality of images, comprising images frommultiple cell angles, help derive extra features from the particle whichwould typically be hidden if a plurality of images are combined into amulti-dimensional reconstruction (e.g., a two-dimensional hologram or athree-dimensional reconstruction). In some examples, the microfluidicsystem may be configured such that the particle (e.g., a cell) isinduced to rotate as it moves through the microfluidic channel (FIG. 4).Examples of a plurality of images 1, 2, 3, and 4 of a rotating cell isshown in FIG. 4.

In some embodiments, the systems and methods of present disclosure allowfor a tracking ability, wherein the system and methods track a particle(e.g., cell) under the camera and maintain the knowledge of which framesbelong to the same particle. In some embodiments, the particle istracked until it has been classified and/or sorted. In some cases, theparticle may be tracked by one or more morphological (e.g., shape, size,area, volume, texture, thickness, roundness, etc.) and/or optical (e.g.,light emission, transmission, reflectance, absorbance, fluorescence,luminescence, etc.) characteristics of the particle. In some examples,each particle may be assigned a score (e.g., a characteristic score)based on the one or more morphological and/or optical characteristics,thereby to track and confirm the particle as the particle travelsthrough the microfluidic channel.

In some embodiments, the systems and methods of the disclosure compriseimaging a single particle in a particular field of view of the camera.In some aspects, the system and methods of the present disclosure imagemultiple particles in the same field of view of camera. Imaging multipleparticles in the same field of view of the camera can provide additionaladvantages, for example it will increase the throughput of the system bybatching the data collection and transmission of multiple particles. Insome instances, at least about 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40,50, 60, 70, 80, 90, 100, or more particles are imaged in the same fieldof view of the camera. In some instances, 100 to 200 particles areimaged in the same field of view of the camera. In some instances, atmost about 100, 90, 80, 70, 60, 50, 40, 30, 20, 10, 9, 8, 7, 6, 5, 4, 3,or 2 particles are imaged in the same field of view of the camera. Insome cases, the number of the particles (e.g., cells) that are imaged inthe same field of view may not be changed throughout the operation ofthe flow cell. Alternatively, the number of the particles (e.g., cells)that are imaged in the same field of view may be changed in real-timethroughout the operation of the flow cell, e.g., to increase speed ofthe classification and/or sorting process without negatively affectingquality or accuracy of the classification and/or soring process.

The imaging region maybe downstream of the focusing region and theordering region. Thus, the imaging region may not be part of thefocusing region and the ordering region. In an example, the focusingregion may not comprise or be operatively coupled to any imaging devicethat is configured to capture one or more images to be used for particleanalysis (e.g., cell classification).

Feature Extraction and Classification

In some embodiments, the analysis of the images and/or classification ofthe particles are performed manually, for example by a human being. Insome embodiments, the classification is performed by one or moreclassifiers. In some embodiments, the classifier is an automatedclassifier. In some embodiments, the classifier is trained to performoutcome determination based on cell feature and/or pattern recognition.The cell features and/or cell patterns include, but are not limited to,recognition of cell shape, cell diameter, nuclear shape, nucleardiameter, nuclear texture, nuclear edges, nuclear area, nuclear averageintensity, nucleus to cytoplasm ratio, cell texture, cell edges, cellarea, cell average intensity, DNA content (pgDNA) of the cells and/orthe like.

In some embodiments, the one or more classifiers used in the system andmethods of the present disclosure are trained using an adaptive labelingapproach. In such training approach a classifier is first trained with afirst set of known cells. The classifier is then allowed to classifycells in a second set of cells, such that the classifier classifies eachcell in the second set of cells with a classification probability,wherein the classification probability is a representation of theclassifier's certainty about the classification of a particular cell.After the classification of the second set of cells, cells classifiedwith a classification probability lower than a predefined threshold areselected. These selected cells will represent cases where the classifieris “unsure” about the class. In some cases, this uncertainty may be dueto sparsity of that particular type of cells in the first set of knowncells. Once, the cells classified with a lower than the pre-definedthreshold have been selected, the classifier is trained in a next roundto classify these selected cells. In some embodiments, these steps canbe repeated till a majority of cells, for e.g. greater than 50% ofcells, greater than 55% of cells, greater than 60% of cells, greaterthan 65% of cells, greater than 70% of cells, greater than 75% of cells,greater than 80% of cells, greater than 85% of cells, greater than 90%of cells, greater than 91% of cells, greater than 91% of cells, greaterthan 92% of cells, greater than 93% of cells, greater than 94% of cells,greater than 95% of cells, greater than 96% of cells, greater than 97%of cells, greater than 98% of cells, greater than 99% of cells, or 100%of cells are classified with the classification probability equal to orgreater than a predefined threshold. The technique is outlined in FIG.7.

In some cases, the classifier of the present disclosure may be providedwith one or more initial distinguishing characteristics (e.g.,morphological features) of the first set of known cells during the firsttraining. The classifier may discover one or more additionalcharacteristics of the first set of known cells after the firsttraining. The additional characteristic(s) may be used (i) inconjunction with or (ii) in place of some of the initial distinguishingcharacteristic(s) during the actual cell classification. In some cases,the classifier may not be provided with any initial distinguishingcharacteristics of the first set of known cells during the firsttraining. The classifier may be allowed to discover one or morecharacteristics of the first set of known cells during the firsttraining, and such characteristic(s) may be used during the actual cellclassification. The deep learning algorithm that utilizes suchclassifier may continuously update and revise the classifier (e.g.,update and revise distinguishing characteristics of target cells) duringmultiple rounds of cell classification and sorting.

In some examples, the predefined threshold is a classificationprobability of greater than 0.50. In some examples, the predefinedthreshold is a classification probability that is greater than 0.60. Insome examples, the predefined threshold is a classification probabilitythat is greater than 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.91, 0.92,0.93, 0.94, 0.95, 0.96, 0.97, 0.98, or greater than 0.99. In someinstances, the threshold probability is a classification probabilityof 1. In various embodiments, the classification probability iscalculated based on validation tests. The validations tests can rangefrom biological validations to software and simulation validations.

In some embodiments, the systems and methods disclosed herein useenhanced neural network designed specifically for cell imageclassification, for e.g. for single cell image classification.Accordingly, provided herein is a general framework that modifies aconvolution neural architecture for single cell image classification. Insome aspects, the enhanced neural network is designed by modification ofknown neural networks designed to work well with ordinary images, suchas the image-net. In some aspects the enhanced neural networks aredesigned by the modification of neural networks such as AlexNet, VGGNet,GoogLeNet, ResNet (residual networks), DenseNet, and Inception networks.In some examples, the enhanced neural networks are designed bymodification of ResNet (e.g. ResNet 18, ResNet 34, ResNet 50, ResNet101, and ResNet 152) or inception networks. In some aspects, themodification comprises a series of network surgery operations that aremainly carried out to improve including inference time and/or inferenceaccuracy.

In some embodiments the modification of the known neural networkscomprises shrinking one or more late stage layers of the known neuralnetwork. In some example, shrinking the one or more late stage layersresults in improved inference time of the neural network. In someinstances, later-stage layers play less important roles in cell imagedata due to the visual features in cell images having lower complexitythan objects found in image-net set or similar datasets.

In some aspects, shrinkage of late-stage layers may result in anaccuracy loss. In some embodiments, such loss may be compensated atleast partially by the addition of back hidden layers to earlier partsof the network. In some examples, shrinking the late stage layers andadding them to an earlier part of the neural network improves accuracyof the classifier even more than keeping them in the late layers.

In various examples, the early vs. late layers are distinguished bytheir distance to the input layer and/or depth.

An exemplary enhanced neural network designed by the methods disclosedherein is shown in FIG. 8. The exemplary enhanced neural network isdesigned by modification of a residual learning network known as ResNet50. In the modification exemplified, one block of 512 depth and twoblocks of 256 depth, both of which are late stage residual layers areremoved, and one block is added to the 128 block, which comes earlier inthe network. In this particular example, the removal of late stageblocks improved the inference time, and the addition of the 128 depthblock gained back and even improved the inference accuracy which wasinitially feasible with the standard ResNet 50.

In some embodiments, the use of the enhanced neural networks asdisclosed herein improves the inference time, wherein the increase inthe inference time is calculated as a percent increase using theformula: Percentage increase in the inference time=(inference time usingthe enhanced neural network-inference time using the known neuralnetwork)/inference time using the known neural network×100. In someembodiments, the use of the enhanced neural networks as disclosed hereinimproves the inference time by at least 10% as compared to the inferencetime with the known network the enhanced network is derived from. Insome examples, the inference time is improved by at least 20%, at least30%, at least 40%, at least 50%, at least 60%, at least 70%, at least80%, at least 90%, at least 100%, at least 150%, at least 200%, at least250%, at least 300%, at least 350%, at least 400%, at least 450%, atleast 500%. In some embodiments, the inference accuracy of the enhancedneural networks is improved by at least 10%, at least 20%, at least 30%,at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, atleast 90%, at least 100%, at least 150%, at least 200%, at least 250%,at least 300%, at least 350%, at least 400%, at least 450%, at least500%.

In some embodiments, the accuracy of the system and/or methods disclosedherein is improved by using multiple images belonging to the same cellto assist and increase the accuracy of a classifier. FIG. 9 illustratesthis embodiment. In some embodiments, the multiple images compriseimages obtained from multiple cell angles. In some embodiments, theimages from the multiple cell angles are captured as the cell isrotating in the microfluidic channel. In some embodiments, the imagesfrom multiple cell angles are captured by moving one or more imagingdevices as the particle passes through the microfluidic channel. In someembodiments, the images from multiple cell angles are captured bymultiple imaging devices positioned to capture particle images fromspecific particle angles. In some embodiments, the images from multiplecell angles are captured by a single imaging device (e.g., a singlehigh-speed camera) to capture particle images from multiple particleangles.

In some embodiments, the accuracy of the systems and methods disclosedherein is further improved by utilizing ensemble methods forclassification. The ensemble methods disclosed herein use a set ofclassifiers which have been trained based on different neural networks,with different initial conditions, etc. The set of classifiers are thenallowed to classify the particle and the results from each of theclassifier in the set of classifiers are aggregated to determine a finalclassification of the particle. In some embodiments at least 2, at least3, at least 4, at least 5, at least 6, at least 7, at least 8, at least9, or at least 10 classifiers are used. In some cases, deep learningalgorithms with ensemble methods may be used for cell classification atmultiple angles. A cell may be transported through a flow channel. Aplurality of images of the cell from a plurality of different angles maybe captured (e.g., due to rotation of the cell, rotational movement ofthe imaging device, or using a plurality of imaging devices).Subsequently, each of the plurality of images may be analyzed using thedeep learning algorithm to classify the cell at each of the plurality ofdifferent images at each of the plurality of different angles. As shownin FIG. 9, the analysis may determine a probability of the cell beingclassified as a specific target cell (e.g., at angle 1, the cell may becell type A with a probability of 0.8, while the cell may be cell type Bwith a probability of 0.1). Afterwards, a plurality of suchclassifications of the cell from the analyzing may be combined (e.g.,using an aggregator function) to determine a final classification of thecell.

In some embodiments, the system and methods of the present disclosureleverage the multi-view nature of the acquired data by concatenatingimages obtained from multiple cell angles in a single training orinference procedure to the classifier. In some aspects, theconcatenating is similar to that carried out in traditional neural netsto concatenate red green blue (RGB) colors. In some aspects, the presentdisclosure concatenates separate images belonging to the same cell orparticle, wherein both training and classification procedures are spedup since only one inference operation is carried out per cell. FIG. 10illustrates this embodiment.

In some cases, the plurality of images of the cell from a plurality ofangles may be analyzed (e.g., fed into one or more deep learningclassifiers) to determine a degree of focus of the cell in each of theplurality of images. In some cases, a probability of the cell being infocus on the image may be determined. The classifiers (e.g., the neuralnetworks operatively coupled to the flow cell) may determine whether thecell is in focus or out of focus (i.e., “blurry”) in each of theplurality of images. The classifiers may be provided with one or moreparameters (e.g., Fourier transformation algorithms, intensitycomparison algorithms, etc.) that can be utilized to determine thedegree of focus of the cell in each picture. Alternatively, theclassifiers may not be provided with such tools, and the classifiers maylearn and discover (“learn”) its own parameters, definitions, orcharacteristics to determine whether the degree of focus of the cell ineach image. When some (or majority) of the plurality of images aredetermined to be out of focus, the deep learning algorithm may updateone or more mechanisms of cell flow (e.g., inertia-based z focusingprovided herein) to adjust position of a streamline of cells within themicrofluidic channel and improve focusing. In some embodiments,different deep learning neural networks may be utilized for theclassification of cells and the focusing analysis of the plurality ofimages. In some embodiments, the same deep learning neural networks maybe utilized for both classification of cells and focusing analysis ofthe plurality of images.

In some cases, one or more parameters of the imaging device(s) (e.g.,depth of field, shutter speed, etc.) may be modified (e.g., in realtime) based on the degree of focus of the cell to improve focusing. Insome cases, one or more other parameters of flow cell (e.g., flowvelocity, co-flow, cell rotation, cell density in the fluid, etc.) maybe modified (e.g., in real time) based on the degree of focus of thecell to improve focusing.

Sorting

In some embodiments, the systems and the methods of the presentdisclosure actively sorts a stream of particles. The term sort orsorting as used herein refers to physically separating particles, fore.g. cells, with one or more desired characteristics. The desiredcharacteristic(s) can comprise a feature of the cell(s) analyzed and/orobtained from the image(s) of the cell. Examples of the feature of thecell(s) can comprise a size, shape, volume, electromagnetic radiationabsorbance and/or transmittance (e.g., fluorescence intensity,luminescence intensity, etc.), or viability (e.g., when live cells areused).

The flow channel can branch into a plurality of channels, and the cellsorting system can be configured to sort the cell by directing the cellto a selected channel of the plurality of channels based on the analyzedimage of the cell. The analyzed image may be indicative of one or morefeatures of the cell, wherein the feature(s) are used as parameters ofcell sorting. In some cases, one or more channels of the plurality ofchannels can have a plurality of sub-channels, and the plurality ofsub-channels can be used to further sort the cells that have been sortedonce.

Cell sorting may comprise isolating one or more target cells from apopulation of cells. The target cell(s) may be isolated into a separatereservoir that keeps the target cell(s) separate from the other cells ofthe population. Cell sorting accuracy may be defined as a proportion(e.g., a percentage) of the target cells in the population of cells thathave been identified and sorted into the separate reservoir. In somecases, the cell sorting accuracy of the flow cell provided herein may beat least 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%,92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or more (e.g., 99.9% or 100%).In some cases, the cell sorting accuracy of the flow cell providedherein may be at most 100%, 99%, 98%, 7%, 96%, 95%, 94%, 93%, 92%, 91%,90%, 89%, 88%, 87%, 86%, 85%, 84%, 83%, 82%, 81%, 80%, or less.

In some cases, cell sorting may be performed at a rate of at least 1cell/second, 5 cells/second, 10 cells/second, 50 cells/second, 100cells/second, 500 cells/second, 1,000 cells/second, 5,000 cells/second,10,000 cells/second, 50,000 cells/second, or more. In some cases, cellsorting may be performed at a rate of at most 50,000 cells/second,10,000 cells/second, 5,000 cells/second, 1,000 cells/second, 500cells/second, 100 cells/second, 50 cells/second, 10 cells/second, 5cells/second, 1 cell/second, or less.

In some aspects, the systems and methods disclosed herein use an activesorting mechanism. In various embodiments, the active sorting isindependent from analysis and decision making platforms and methods. Invarious embodiments the sorting is performed by a sorter, which receivesa signal from the decision making unit (e.g. a classifier), or any otherexternal unit, and then sorts cells as they arrive at the bifurcation.The term bifurcation as used herein refers to the termination of theflow channel into two or more channels, such that cells with the one ormore desired characteristics are sorted or directed towards one of thetwo or more channels and cell without the one or more desiredcharacteristics are directed towards the remaining channels. In someembodiments, the flow channel terminates into at least 2, 3, 4, 5, 6, 7,8, 9, 10, or more channels. In some embodiments, the flow channelterminates into at most 10, 9, 8, 7, 6, 5, 4, 3, or 2 channels. In someembodiments, the flow channel terminates in two channels and cells withone or more desired characteristics are directed towards one of the twochannels (the positive channel), while cells without the one or moredesired characteristics are directed towards the other channel (thenegative channel). In some embodiments, the flow channel terminates inthree channels and cells with a first desired characteristic aredirected to one of the three channels, cells with a second desiredcharacteristic are directed to another of the three channels, and cellswithout the first desired characteristic and the second desiredcharacteristic are directed to the remaining of the three channels.

In some embodiments, the sorting is performed by a sorter. The sortermay function by predicting the exact time at which the particle willarrive at the bifurcation. To predict the time of particle arrival, thesorter can use any applicable method. In some examples, the sorterpredicts the time of arrival of the particle by using (i) velocity ofparticles (e.g., downstream velocity of a particle along the length ofthe microfluidic channel) that are upstream of the bifurcation and (ii)the distance between velocity measurement/calculation location and thebifurcation. In some examples, the sorter predicts the time of arrivalof the particles by using a constant delay time as an input.

In some cases, prior to the cell's arrival at the bifurcation, thesorter may measure the velocity of a particle (e.g., a cell) at least 1,2, 3, 4, 5, or more times. In some cases, prior to the cell's arrival atthe bifurcation, the sorter may measure the velocity of the particle atmost 5, 4, 3, 2, or 1 time. In some cases, the sorter may use at least1, 2, 3, 4, 5, or more sensors. In some cases, the sorter may use atmost 5, 4, 3, 2, or 1 sensor. Example of the sensor(s) may be an imagingdevice (e.g., a camera such as a high-speed camera), one- or multi-pointlight (e.g., laser) detector, etc. In some cases, the sorter may use anyone of the imaging devices (e.g., the high-speed camera system 114)disposed at or adjacent to the imaging region 138. In some examples, thesame imaging device(s) may be used to capture one or more images of acell as the cell is rotating and migrating within the channel, and theone or more images may be analyzed to (i) classify the cell and (ii)measure a rotational and/or lateral velocity of the cell within thechannel and predict the cell's arrival time at the bifurcation. In someexamples, the sorter may use one or more sensors that are different thanthe imaging devices of the imaging region 138. The sorter may measurethe velocity of the particle (i) upstream of the imaging region 138,(ii) at the imaging region 138, and/or (iii) downstream of the imagingregion 138.

The sorter may comprise or be operatively coupled to a processor, suchas a computer processor. Such processor may be the processor 116 that isoperatively coupled to the imaging device 114 or a different processor.The processor may be configured to calculate the velocity of a particle(rotational and/or downstream velocity of the particle) an predict thetime of arrival of the particle at the bifurcation. The processor may beoperatively coupled to one or more valves of the bifurcation. Theprocessor may be configured to direct the valve(s) to open and close anychannel in fluid communication with the bifurcation. The processor maybe configured to predict and measure when operation of the valve(s)(e.g., opening or closing) is completed.

In some examples, the sorter may comprise a self-included unit (e.g.,comprising the sensors, such as the imaging device(s)) which is capableof (i) predicting the time of arrival of the articles and/or (ii)detecting the particle as it arrives at the bifurcation. In order tosort the particles, the order at which the particles arrive at thebifurcation, as detected by the self-included unit, may be matched tothe order of the received signal from the decision making unit (e.g. aclassifier). In some aspects, controlled particles are used to align andupdate the order as necessary. In some examples, the decision makingunit may classify a first cell, a second cell, and a third cell,respectively, and the sorter may confirm that the first cell, the secondcell, and the third cell are sorted, respectively in the same order. Ifthe order is confirmed, the classification and sorting mechanisms (ordeep learning algorithms) may remain the same. If the order is differentbetween the classifying and the sorting, then the classification and/orsorting mechanisms (or deep learning algorithms) may be updated oroptimized, either manually or automatically. In some aspects, thecontrolled particles may be cells (e.g., live or dead cells).

In some aspects, the controlled particles may be special calibrationbeads (e.g., plastic beads, metallic beads, magnetic beads, etc.). Insome embodiments the calibration beads used are polystyrene beads withsize ranging between about 1 μM to about 50 μM. In some embodiments thecalibration beads used are polystyrene beads with size of least about 1μM. In some embodiments the calibration beads used are polystyrene beadswith size of at most about 50 μM. In some embodiments the calibrationbeads used are polystyrene beads with size ranging between about 1 μM toabout 3 μM, about 1 μM to about 5 μM, about 1 μM to about 6 μM, about 1μM to about 10 μM, about 1 μM to about 15 μM, about 1 μM to about 20 μM,about 1 μM to about 25 μM, about 1 μM to about 30 μM, about 1 μM toabout 35 μM, about 1 μM to about 40 μM, about 1 μM to about 50 μM, about3 μM to about 5 μM, about 3 μM to about 6 μM, about 3 μM to about 10 μM,about 3 μM to about 15 μM, about 3 μM to about 20 μM, about 3 μM toabout 25 μM, about 3 μM to about 30 μM, about 3 μM to about 35 μM, about3 μM to about 40 μM, about 3 μM to about 50 μM, about 5 μM to about 6μM, about 5 μM to about 10 μM, about 5 μM to about 15 μM, about 5 μM toabout 20 μM, about 5 μM to about 25 μM, about 5 μM to about 30 μM, about5 μM to about 35 μM, about 5 μM to about 40 μM, about 5 μM to about 50μM, about 6 μM to about 10 μM, about 6 μM to about 15 μM, about 6 μM toabout 20 μM, about 6 μM to about 25 μM, about 6 μM to about 30 μM, about6 μM to about 35 μM, about 6 μM to about 40 μM, about 6 μM to about 50μM, about 10 μM to about 15 μM, about 10 μM to about 20 μM, about 10 μMto about 25 μM, about 10 μM to about 30 μM, about 10 μM to about 35 μM,about 10 μM to about 40 μM, about 10 μM to about 50 μM, about 15 μM toabout 20 μM, about 15 μM to about 25 μM, about 15 μM to about 30 μM,about 15 μM to about 35 μM, about 15 μM to about 40 μM, about 15 μM toabout 50 μM, about 20 μM to about 25 μM, about 20 μM to about 30 μM,about 20 μM to about 35 μM, about 20 μM to about 40 μM, about 20 μM toabout 50 μM, about 25 μM to about 30 μM, about 25 μM to about 35 μM,about 25 μM to about 40 μM, about 25 μM to about 50 μM, about 30 μM toabout 35 μM, about 30 μM to about 40 μM, about 30 μM to about 50 μM,about 35 μM to about 40 μM, about 35 μM to about 50 μM, or about 40 μMto about 50 μM. In some embodiments the calibration beads used arepolystyrene beads with size of about 1 μM, about 3 μM, about 5 μM, about6 μM, about 10 μM, about 15 μM, about 20 μM, about 25 μM, about 30 μM,about 35 μM, about 40 μM, or about 50 μM.

In some embodiments, the sorter (or an additional sensor disposed at oradjacent to the bifurcation) may be configured to validate arrival ofthe particles (e.g., the cells) at the bifurcation. In some examples,the sorter may be configured to measure an actual arrival time of theparticles (e.g., the cells) at the bifurcation. The sorter may analyze(e.g., compare) the predicted arrival time, the actual arrival time, thevelocity of the particles downstream of the channel prior to anyadjustment of the velocity, and/or a velocity of the particlesdownstream of the channel subsequent to such adjustment of the velocity.Based on the analyzing, the sorter may modify any operation (e.g., cellfocusing, cell rotation, controlling cell velocity, cell classificationalgorithms, valve actuation processes, etc.) of the flow cell. Thevalidation by the sorter may be used for closed-loop and real-timeupdate of any operation of the flow cell.

In some embodiments, the sorters used in the systems and methodsdisclosed herein are self-learning cell sorting systems or intelligentcell sorting systems. These sorting systems can continuously learn basedon the outcome of sorting. For example, a sample of cells is sorted, thesorted cells are analyzed, and the results of this analysis are fed backto the classifier. In some examples, the cells that are sorted as“positive” (i.e., target cells or cells of interest) may be analyzed andvalidated. In some examples, the cells that are sorted as “negative”(i.e., non-target cells or cells not of interest) may be analyzed andvalidated. In some examples, both positive and negative cells may bevalidated. Such validation of sorted cells (e.g., based on secondaryimaging and classification) may be used for closed-loop and real-timeupdate of the primary cell classification algorithms.

In some aspects, the sorters used in the systems and methods disclosedherein do not rely on “supervised labels.” In some aspects, the sorterscan identify “different looking” sub-populations in a sample, whereinthe system automatically identifies and clusters different cell types,in the way of unsupervised learning or semi-supervised learning, byrunning and collecting their features, thus, by imaging them. In someaspects, the system works by running the sample for a few seconds,wherein the classifier then “learns” the components of the sample, thenthe actual sorting of those classes begins. In some aspects, the systemis a sorter system where the sorting decision is made by artificialintelligence (AI), such as deep learning.

In some embodiments, the system and methods of the present disclosurecomprise a cascaded sorting system, wherein a first step bulk sorting iscarried out, wherein a subgroup of particles in a sample are monitoredsequentially or simultaneously for a positive particle (for example fora cell with one or more desired characteristics), and if any positiveparticle is detected in the subgroup of particles then, the subgroup ofparticles is subjected to further sorting. In some embodiments, thecascade sorting comprises capturing an image of a subgroup of aplurality of cells as the plurality of cells pass through a first flowchannel; and analyzing the image for a feature. Following which, if thefeature is detected then one or more cells in the subgroup are imagedagain. In some embodiments, the first step comprises imaging 10 to 100cells at a time and if any positive cell is detected, the group of 10 to100 cells is sent to one side of the bifurcation and analyzed again. Insome aspects, the first step comprises imaging 20 to 100 cells at atime. In some aspects, the first step comprises imaging 30 to 100 cellsat a time. In some aspects, the first step comprises imaging 40 to 100cells at a time. In some aspects, the first step comprises imaging 50 to100 cells monitored at a time. In some aspects, the first step comprisesimaging 50 to 100 cells at a time. In some aspects, the first stepcomprises imaging 60 to 100 cells at a time. In some aspects, the firststep comprises imaging 70 to 100 cells at a time. In some aspects, thefirst step comprises imaging 80 to 100 cells at a time. In some aspects,the first step comprises imaging 90 to 100 cells at a time. In someaspects, the first step is followed by a next phase sorting of thesubgroup of cells according to the methods described herein. In someembodiments, the next phase sorting is performed by directing thesubgroup of cells to a second flow channel and by imaging the cells inthe subgroup, single cell at a time according to the methods describedherein.

In some examples, a sample of cells may be divided into multiplesubgroups of cells. A size of each subgroup of cells may be at least 10,20, 30, 40, 50, 60, 70, 80, 90, 100, or more cells. The size of eachsubgroup of cells may be at most 100, 90, 80, 70, 60, 50, 40, 30, 20,10, or less cells. In a first phase of the cascade sorting, the subgroupof cells may be imaged and analyzed to detect presence of a target cell.If a number of the target cell in the subgroup (or a proportion thereofin the subgroup) is above a predetermined threshold value, then theentire subgroup of cells may be collected or directed for a second phaseof the cascade sorting. The predetermined threshold value may be atleast 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more cells. The predeterminedthreshold value may be at most 10, 9, 8, 7, 6, 5, 4, 3, 2, or 1 cell.The predetermined proportion may be at least 1%, 2%, 3%, 4%, 5%, 6%, 7%,8%, 9%, 10%, or more of the subgroup. The predetermined proportion maybe at most 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1%, or less of thesubgroup. In some examples, the entire subgroup may be analyzed duringthe first phase before making the decision to proceed to the secondphase. In some examples, only a portion of the subgroup may be analyzed,and when the predetermined threshold value is reached, the subgroup ofcells may be directed to the second phase. During the second phase, thesubgroup of cells may be directed back to the same imaging region forclassification and sorting of each of the subgroup of cells.Alternatively, the subgroup of cells may be directed to a differentimaging region for classification and sorting of each of the subgroup ofcells. In another alternative, the subgroup of cells may be collectedand stored (e.g., in a reservoir in fluid communication with the flowcell or in a separate reservoir) until the entire sample of cellsundergo the first phase of the cascade sorting.

Sorting Techniques

In some embodiments, the methods and systems disclosed herein can useany sorting technique to sort particles. An exemplary design for sortingthe cells is shown in FIG. 11, wherein desired cell are directedto/sorted into the positive collection reservoir. At least a portion ofthe collection reservoir may or may not be pre-filled with a fluid,e.g., a buffer. In some embodiments, the sorting technique comprisesclosing a channel on one side of the bifurcation to collect the desiredcell on the other side. In some aspects, the closing of the channels canbe carried out by employing any known technique. In some aspects, theclosing is carried out by application of a pressure. In some instances,the pressure is pneumatic actuation. In some aspects, the pressure canbe positive pressure or negative pressure. In some embodiments, positivepressure is used. In some examples, one side of the bifurcation isclosed by applying pressure and deflecting the soft membrane between topand bottom layers. Pneumatic actuation is further described inWO2001001025A2, which is incorporated herein by reference in itsentirety.

In some aspects, the particles to be sorted are directed to one side ofthe bifurcation by increasing the fluidic resistance on the other side.For example, if one side of the bifurcation can be designed to have alonger channel compared to other side. In such embodiments, the cellsnormally go to the other (shorter) channel even when both valves areopen. In such cases, the cells will go to the longer channel only whenthe valve on the shorter channel is closed.

In some embodiments, the closing of a channel on one side of thebifurcation is carried out by application of an electric filed. In someexamples, the closing of a channel on one side of the bifurcation isachieved by using piezo actuator. For example, a piezo actuator can beused along to the channel. When a voltage is added to the piezo, thepiezo deforms by elongating and/or pinching the channel cross section. Aschematic representation of the use of piezo actuator is shown in FIG.12. In some instances, a deformable material is used for themicrofluidic channels, for example polydimethylsiloxane (PDMS) is used.

In some aspects, the closing of a channel on one side of the bifurcationis carried out by application of a magnetic field. In some examples, theclosing of a channel on one side of the bifurcation is achieved by usingmagnetic actuation. For example, an electromagnet (EM) can be used alongone side of the channel and a magnet can be used on the other side ofthe channel. In some instances, when the electromagnet is activated, themagnet becomes attracted to the electromagnet, leaving the channelcross-section to be pinched and closed. In some instances, a deformablematerial, for example is used for polydimethylsiloxane (PDMS) for themicrofluidic channels shown in (FIG. 13).

Sample and Data Collection

In various embodiments, the systems and methods of the presentdisclosure comprise one or more reservoirs designed to collect theparticles after the particles have been sorted. In some embodiments, thenumber of cells to be sorted is about 1 cell to about 1,000,000 cells.In some embodiments, the number of cells to be sorted is at least about1 cell. In some embodiments, the number of cells to be sorted is at mostabout 1,000,000 cells. In some embodiments, the number of cells to besorted is about 1 cell to about 100 cells, about 1 cell to about 500cells, about 1 cell to about 1,000 cells, about 1 cell to about 5,000cells, about 1 cell to about 10,000 cells, about 1 cell to about 50,000cells, about 1 cell to about 100,000 cells, about 1 cell to about500,000 cells, about 1 cell to about 1,000,000 cells, about 100 cells toabout 500 cells, about 100 cells to about 1,000 cells, about 100 cellsto about 5,000 cells, about 100 cells to about 10,000 cells, about 100cells to about 50,000 cells, about 100 cells to about 100,000 cells,about 100 cells to about 500,000 cells, about 100 cells to about1,000,000 cells, about 500 cells to about 1,000 cells, about 500 cellsto about 5,000 cells, about 500 cells to about 10,000 cells, about 500cells to about 50,000 cells, about 500 cells to about 100,000 cells,about 500 cells to about 500,000 cells, about 500 cells to about1,000,000 cells, about 1,000 cells to about 5,000 cells, about 1,000cells to about 10,000 cells, about 1,000 cells to about 50,000 cells,about 1,000 cells to about 100,000 cells, about 1,000 cells to about500,000 cells, about 1,000 cells to about 1,000,000 cells, about 5,000cells to about 10,000 cells, about 5,000 cells to about 50,000 cells,about 5,000 cells to about 100,000 cells, about 5,000 cells to about500,000 cells, about 5,000 cells to about 1,000,000 cells, about 10,000cells to about 50,000 cells, about 10,000 cells to about 100,000 cells,about 10,000 cells to about 500,000 cells, about 10,000 cells to about1,000,000 cells, about 50,000 cells to about 100,000 cells, about 50,000cells to about 500,000 cells, about 50,000 cells to about 1,000,000cells, about 100,000 cells to about 500,000 cells, about 100,000 cellsto about 1,000,000 cells, or about 500,000 cells to about 1,000,000cells. In some embodiments, the number of cells to be sorted is about 1cell, about 100 cells, about 500 cells, about 1,000 cells, about 5,000cells, about 10,000 cells, about 50,000 cells, about 100,000 cells,about 500,000 cells, or about 1,000,000 cells.

In some embodiments, the number of cells to be sorted is 100 to 500cells, 200 to 500 cells, 300 to 500 cells, 350 to 500 cells, 400 to 500cells, or 450 to 500 cells. In some embodiments, the reservoirs may bemilliliter scale reservoirs. In some examples, the one or morereservoirs are pre-filled with a buffer and the sorted cells are storedin the buffer. Using the buffer helps to increase the volume of thecells, which can then be easily handled, for example a pipetted. In someexamples, the buffer is a phosphate buffer, for examplephosphate-buffered saline (PBS).

In some embodiments, the system and methods of the present disclosurecomprise a cell sorting technique wherein pockets of buffer solutioncontaining no negative objects are sent to the positive output channelin order to push rare objects out of the collection reservoir. In someaspects, additional buffer solution is sent to the positive outputchannel to flush out all positive objects at the end of a run, once thechannel is flushed clean.

Real-Time Integration

In some embodiments, the system and methods of the present disclosurecomprise a combination of techniques, wherein a graphics processing unit(GPU) and a digital signal processor (DSP) are used to run artificialintelligence (AI) algorithms and apply classification results inreal-time to the system. In some aspects, the system and methods of thepresent disclosure comprise a hybrid method for real-time cell sorting.

Validation

In some embodiments, the systems disclosed herein further comprise avalidation unit that detects the presence of a particle without gettingdetailed information, such as imaging. In some instances, the validationunit may be used for one or more purposes. In some examples, thevalidation unit detects a particle approaching the bifurcation andenables precise sorting. In some examples, the validation unit detects aparticle after the particle has been sorted to one of subchannels influid communication with the bifurcation. In some examples, thevalidation unit provides timing information with a plurality of laserspots, e.g., two laser spots. In some instances, the validation unitprovides timing information by referencing the imaging time. In someinstances, the validation unit provides precise time delay informationand/or flow speed of particles.

In some embodiments, the validation unit is a laser-assisted system. Insome aspects, the laser-assisted system measures the laser blockage orscattering, and thereby the size of the particle may be inferred. Insome aspects, the laser-assisted system measures the laser blockagescattering, and thereby other physical information of the particle maybe inferred, including but not limited to the size, the shape, thedensity, and the texture. In some aspects, the laser-assisted systemmeasures the laser blockage or scattering, and thereby the transientspeed of the object may be inferred.

In some embodiments, the validation unit, for e.g. the laser-assistedsystem, is located after the bifurcation (for e.g. on the positive side,on the negative side, or on both sides). In some examples, such alocation of the validation unit provides clarification information onthe accuracy and effectiveness of the sorting. In some aspects, thevalidation system may be used to create a closed feedback loop to adjustthe control parameters of the system. In some instances, the closedfeedback loop can be used to improve system accuracy and/or toaccommodate slow system draft over the course of the experimental run.

In some embodiments, the validation unit comprises a laser-assistedsystem wherein two or more laser spots are utilized. When the two ormore laser spots are closely placed, different methods, disclosedherein, are used to create the two or more laser spots and laterseparate into two or more independent signals for photo detection. Insome embodiments, the distance between the two or more closely spacedlaser spots is in the range of 10 μm-about 1,000 μm, for example thedistance is between 10 μm to 500 μm, between 50 μm to 500 μm, 100 μm to500 μm, 150 μm to 500 μm, between 200 μm to 500 μm, between 250 μm to500 μm, between 300 μm to 500 μm, between 350 μm to 500 μm, between 400μm to 500 μm, or between 450 μm to 500 μm.

In some embodiments, the two or more closely spaced laser spots comefrom different optical trains with their respective tilt angle. In someaspects, the two or more closely spaced lasers pass through the samefocusing optics but come in with slightly different angles and thus, getfocused with slight offset to each other. In some aspects, the two ormore closely spaced laser spots have different wavelengths and arerecombined with slight offset through a dichroic mirror that passes somelasers while reflecting the other lasers, wherein with the properarrangement, the lasers can come out together with the desired offset.In some aspects, the two or more lasers spots have differentpolarizations and can be recombined with a desired offset through apolarization beam splitter. In some aspects, the two or more closelyspaced lasers are generated by recombining with a close proximitythrough a general purpose beam-splitter as shown in FIG. 14A.

In some embodiments, two light spots (e.g., two closely packed laserspots) may be created by utilizing at least one optical source (e.g., atleast one diode-based laser) and a prism (e.g., Wollaston or RochonPrism) to split two polarizations of a light into two beams emerging ata desired angular separation. The two beams (e.g., the two ellipticalbeams) may be focused onto a same focal plane, separated by a desireddistance between the two focused spots. The two beams may be parallel toeach other. In some cases, absent any beam shaping optics, the focusedspots may be of elliptical in shape, and oriented along the same axis.The distance between the two focused spots may be determined by theprism, the focusing lens, and/or other intermediate optics. Theseparation between the two focused spots may be controlled by properchoice of the prism (e.g., the Wollaston/Rochon prism) and/or the focallength of the focusing lens. FIG. 14B illustrates an exampleconfiguration of a laser diode, a Wollaston prism, and a focusing lensthat are configured with respect to one another to create two focus andparallel spots (e.g., ellipsoidal spots) on the same focal plane.

In some embodiments, due to a shape of the light provided by the opticallight source (e.g., the elliptical shape of the beam from a non-VCSEL(Vertical-Cavity Surface-Emitting Lasers) laser diode), an outline ofone or more resulting focused spots may exhibit a similar shape (e.g.,elliptical). In some cases, the shape of the resulting focused spots maybe modified with one or more beam shaping optical components, e.g.,cylindrical lenses, Powell lenses, anamorphic lenses, etc. The anglebetween the two focused spots (e.g., the two elliptically shaped focusedspots) can be further controlled by utilizing a prism or a pair ofprisms, such as a Dove or similar prism, in one or both legs of theoptical paths, thereby to rotate the beam along that path by the desiredangle. In an example, a major axis of one of the two focused spots canbe rotated (i.e., clocked) to be perpendicular to that of the other ofthe two focused spots by inserting a Dove prism in one leg of the twobeams, while, optionally, inserting a compensation optic in the otherleg of the optical path. Such a configuration of non-parallel laserspots can be utilized in detecting cells in “L” channel junctions.Similarly, different clocking angles generated by inserting appropriatetype and/or number of prisms can be utilized to detect cells in “Y” orother channel junction configurations. FIG. 14C illustrates an exampleconfiguration of a laser diode, a Wollaston prism, a rotating prism(e.g., a Dove prism) along a bottom beam path, a compensating opticalelement along a top beam path, and a focusing lens in opticalcommunication with both top and bottom beam paths to create two focusand parallel spots (e.g., ellipsoidal spots) on the same focal plane.

In some embodiments, the signals from the two or more closely spacedlasers can be separated by reversing the creation methods describedherein. In some aspects, the signals of the two or more lasers can beseparated using optical components with a good alignment. In someinstances, the optical component is a round glass ball. In some aspects,the signals of the two or more lasers can be separated by sending thesignal to a position-sensitive detector. In some instances, theposition-sensitive detector is a dual photodetector. In some instances,the position-sensitive detector is a quad photodetector. In someaspects, the present disclosure comprises a laser-assisted systemwherein two closely spaced laser spots are utilized. In some aspects,the laser spots comprise a slit in between, wherein the change indifferent detection area is used to calculate the original two lasersignal changes.

In some embodiments, the validation of particle sorting by the methodsand the system disclosed herein is performed by the using the backchannel design as shown in FIG. 15. In these embodiments, the validationcomprises imaging both the flow channel where the cells are imaged forthe first time, known as the forward channel, and the outlet, where thecells flow after being sorted. In some aspects, the outlet is designedas a backchannel that fits in the same field of view as the imagingdevice used to image the particles in the flow channel. In some aspects,the outlet particles can be identified against the forward channelparticles by their location and/or velocity. In some instances, thevelocity of the particles is positive in the forward channel. In someinstances, the velocity of the particles is negative in the backwardchannel.

Additional Analysis

Sorted particles (e.g., “positive” or “negative” cells) may be subjectto additional treatment and/or analysis steps. The additional treatmentand/or analysis may include, but are not limited to, cell culture (e.g.,proliferation, differentiation, etc.), cell permeabilization andfixation, cell staining by a probe, mass cytometry, multiplexed ion beamimaging (MIBI), confocal imaging, nucleic acid (e.g., DNA, RNA) orprotein extraction, polymerase chain reaction (PCR), target nucleic acidenrichment, sequencing, sequence mapping, etc. In some cases, the sortedparticles may be transferred (e.g., manually) to one or more instrumentsconfigured to execute any one of the additional treatment and/oranalysis steps. In some cases, the flow channel may be in fluidcommunication with such instrument(s) for automated transfer andexecution of any one of the additional treatment and/or analysis steps.

Examples of the probe used for cell staining (or tagging) may include,but are not limited to, a fluorescent probe (e.g., for stainingchromosomes such as X, Y, 13, 18 and 21 in fetal cells), a chromogenicprobe, a direct immunoagent (e.g. labeled primary antibody), an indirectimmunoagent (e.g., unlabeled primary antibody coupled to a secondaryenzyme), a quantum dot, a fluorescent nucleic acid stain (such as DAPI,Ethidium bromide, Sybr green, Sybr gold, Sybr blue, Ribogreen,Picogreen, YoPro-1, YoPro-2 YoPro-3, YOYo, Oligreen acridine orange,thiazole orange, propidium iodine, or Hoeste), another probe that emitsa photon, or a radioactive probe.

In some cases, the instrument(s) for the additional analysis maycomprise a computer executable logic that performs karyotyping, in situhybridization (ISH) (e.g., florescence in situ hybridization (FISH),chromogenic in situ hybridization (CISH), nanogold in situ hybridization(NISH)), restriction fragment length polymorphism (RFLP) analysis,polymerase chain reaction (PCR) techniques, flow cytometry, electronmicroscopy, quantum dot analysis, or detects single nucleotidepolymorphisms (SNPs) or levels of RNA.

Samples

In some embodiments, the particles (for e.g. cells) analyzed by thesystems and methods disclosed herein are comprised in a sample. Thesample may be a biological sample obtained from a subject. In someembodiments, the biological sample comprises a biopsy sample from asubject. In some embodiments, the biological sample comprises a tissuesample from a subject. In some embodiments, the biological samplecomprises liquid biopsy from a subject. In some embodiments, thebiological sample can be a solid biological sample, e.g., a tumorsample. In some embodiments, a sample from a subject can comprise atleast about 1%, at least about 5%, at least about 10%, at least about15%, at least about 20%, at least about 25%, at least about 30%, atleast about 35%, at least about 40%, at least about 45%, at least about50%, at least about 55%, at least about 60%, at least about 65%, atleast about 70%, at least about 75%, at least about 80%, at least about85%, at least about 90%, at least about 95%, at least about 96%, atleast about 97%, at least about 98%, at least about 99%, or at leastabout 100% tumor cells from a tumor.

In some embodiments, the sample can be a liquid biological sample. Insome embodiments, the liquid biological sample can be a blood sample(e.g., whole blood, plasma, or serum). A whole blood sample can besubjected to separation of cellular components (e.g., plasma, serum) andcellular components by use of a Ficoll reagent. In some embodiments, theliquid biological sample can be a urine sample. In some embodiments, theliquid biological sample can be a perilymph sample. In some embodiments,the liquid biological sample can be a fecal sample. In some embodiments,the liquid biological sample can be saliva. In some embodiments, theliquid biological sample can be semen. In some embodiments, the liquidbiological sample can be amniotic fluid. In some embodiments, the liquidbiological sample can be cerebrospinal fluid. In some embodiments, theliquid biological sample can be bile. In some embodiments, the liquidbiological sample can be sweat. In some embodiments, the liquidbiological sample can be tears. In some embodiments, the liquidbiological sample can be sputum. In some embodiments, the liquidbiological sample can be synovial fluid. In some embodiments, the liquidbiological sample can be vomit.

In some embodiments, samples can be collected over a period of time andthe samples may be compared to each other or with a standard sampleusing the systems and methods disclosed herein. In some embodiments thestandard sample is a comparable sample obtained from a differentsubject, for example a different subject that is known to be healthy ora different subject that is known to be unhealthy. Samples can becollected over regular time intervals, or can be collectedintermittently over irregular time intervals.

In some embodiments, the subject may be an animal (e.g., human, rat,pig, horse, cow, dog, mouse). In some instances, the subject is a humanand the sample is a human sample. The sample may be a fetal humansample. The sample may be from a multicellular tissue (e.g., an organ(e.g., brain, liver, lung, kidney, prostate, ovary, spleen, lymph node,thyroid, pancreas, heart, skeletal muscle, intestine, larynx, esophagus,and stomach), a blastocyst). The sample may be a cell from a cellculture. In some sample the subject is a pregnant human, or a humansuspected to be pregnant.

The sample may comprise a plurality of cells. The sample may comprise aplurality of the same type of cell. The sample may comprise a pluralityof different types of cells. The sample may comprise a plurality ofcells at the same point in the cell cycle and/or differentiationpathway. The sample may comprise a plurality of cells at differentpoints in the cell cycle and/or differentiation pathway.

The plurality of samples may comprise one or more malignant cell. Theone or more malignant cells may be derived from a tumor, sarcoma, orleukemia.

The plurality of samples may comprise at least one bodily fluid. Thebodily fluid may comprise blood, urine, lymphatic fluid, saliva. Theplurality of samples may comprise at least one blood sample.

The plurality of samples may comprise at least one cell from one or morebiological tissues. The one or more biological tissues may be a bone,heart, thymus, artery, blood vessel, lung, muscle, stomach, intestine,liver, pancreas, spleen, kidney, gall bladder, thyroid gland, adrenalgland, mammary gland, ovary, prostate gland, testicle, skin, adipose,eye or brain.

The biological tissue may comprise an infected tissue, diseased tissue,malignant tissue, calcified tissue or healthy tissue.

Non-Invasive Prenatal Testing (NIPT)

Conventional prenatal screening methods for detecting fetalabnormalities and for sex determination use fetal samples acquiredthrough invasive techniques, such as amniocentesis and chorionic villussampling (CVS). Ultrasound imaging is also used to detect structuralmalformations such as those involving the neural tube, heart, kidney,limbs and the like. Chromosomal aberrations such as the presence ofextra chromosomes, such as Trisomy 21 (Down syndrome), Klinefelter'ssyndrome, Trisomy 13 (Patau syndrome), Trisomy 18 (Edwards syndrome), orthe absence of chromosomes, such as Turner's syndrome, or varioustranslocations and deletions can be currently detected using CVS and/oramniocentesis. Both techniques require careful handling and present adegree of risk to the mother and to the pregnancy.

Prenatal diagnosis is offered to women over the age of 35 and/or womenwho are known to carry genetic diseases, as balanced translocations ormicrodeletions.

Chorionic villus sampling (CVS) is performed between the 9^(th) and the14^(th) week of gestation. CVS involves the insertion of a catheterthrough the cervix or the insertion of a needle into the abdomen of thesubject/patient. The needle or catheter is used to remove a small sampleof the placenta, known as the chorionic villus. The fetal karyotype isthen determined within one to two weeks of the CVS procedure. Due to theinvasive nature of the CVS procedure, there is a 2 to 4%procedure-related risk of miscarriage. CVS is also associated with anincreased risk of fetal abnormalities, such as defective limbdevelopment, which are presumably due to hemorrhage or embolism from theaspirated placental tissues.

Amniocentesis is performed between the 16^(th) and the 20^(th) week ofgestation. Amniocentesis involves the insertion of a thin needle throughthe abdomen into the uterus of the patient. This procedure carries a 0.5to 1% procedure-related risk of miscarriage. Amniotic fluid is aspiratedby the needle and fetal fibroblast cells are further cultured for 1 to 2weeks, following which they are subjected to cytogenetic and/orfluorescence in situ hybridization (FISH) analyses.

Recent techniques have been developed to predict fetal abnormalities andpredict possible complications in pregnancy. These techniques usematerial blood or serum samples and have focused on the use of threespecific markers, including alpha-fetoprotein (AFP), human chorionicgonadotrophin (hCG), and estriol. These three markers are used to screenfor Down's syndrome and neural tube defects. Maternal serum is currentlybeing used for biochemical screening for chromosomal aneuploidies andneural tube defects.

The passage of nucleated cells between the mother and fetus is awell-studied phenomenon. Using the fetal cells that are present inmaternal blood for non-invasive prenatal diagnosis prevents the risksthat are usually associated with conventional invasive techniques. Fetalcells include fetal trophoblasts, leukocytes, and nucleated erythrocytesfrom the maternal blood during the first trimester of pregnancy. Thisthe, the isolation of trophoblasts from the maternal blood is limited bytheir multinucleated morphology and the availability of antibodies,whereas the isolation of leukocytes is limited by the lack of uniquecell markers which differentiate maternal from fetal leukocytes.Furthermore, since leukocytes may persist in the maternal blood for aslong as 27 years, residual cells are likely to be present in thematernal blood from previous pregnancies.

In some embodiments, the system and methods disclosed herein are usedfor non-invasive prenatal testing (NIPT), wherein the methods are usedto analyze maternal serum or plasma samples from a pregnant female. Insome aspects, the system and methods are used for non-invasive prenataldiagnosis. In some aspects, the system and methods disclosed herein canbe used to analyze maternal serum or plasma samples derived frommaternal blood. In some aspects, as little as 10 μL of serum or plasmacan be used. In some aspects, larger samples are used to increaseaccuracy, wherein the volume of the sample used is dependent upon thecondition or characteristic being detected.

In some embodiments, the system and methods disclosed herein are usedfor non-invasive prenatal diagnosis including but not limited to sexdetermination, blood typing and other genotyping, detection ofpre-eclampsia in the mother, determination of any maternal or fetalcondition or characteristic related to either the fetal DNA itself orthe quantity or quality of the fetal DNA in the maternal serum orplasma, and identification of major or minor fetal malformations orgenetic diseases present in a fetus. In some aspects, fetus is a humanfetus.

In some embodiments, the system and methods disclosed herein are used toanalyze serum or plasma from maternal blood samples, wherein the serumor plasma preparation is carried out by standard techniques andsubjected to a nucleic acid extraction process. In some aspects, theserum or plasma is extracted using a proteinase K treatment followed byphenol/chloroform extraction.

In some embodiments, the system and methods disclosed herein are used toimage cells from maternal serum or plasma acquired from a pregnantfemale subject. In some aspects, the subject is a human. In someaspects, the pregnant female human subject is over the age of 35. Insome aspects, the pregnant female human subject is known to carry agenetic disease. In some aspects, the subject is a human. In someaspects, the pregnant female human subject is over the age of 35 and isknown to carry a genetic disease.

In some embodiments, the system and methods disclosed herein are used toanalyze fetal cells from maternal serum or plasma. In some aspects, thecells that are used for non-invasive prenatal testing using the systemand methods disclosed herein are fetal cells such as fetal trophoblasts,leukocytes, and nucleated erythrocytes. In some aspects, fetal cells arefrom the maternal blood during the first trimester of pregnancy.

In some embodiments, the system and methods disclosed herein are usedfor non-invasive prenatal diagnosis using fetal cells comprisingtrophoblast cells. In some aspects, trophoblast cells using the presentdisclosure are retrieved from the cervical canal using aspiration. Insome aspects, trophoblast cells using the present disclosure areretrieved from the cervical canal using cytobrush or cotton wool swabs.In some aspects, trophoblast cells using the present disclosure areretrieved from the cervical canal using endocervical lavage. In someaspects, trophoblast cells using the present disclosure are retrievedfrom the cervical canal using intrauterine lavage.

In some embodiments, the system and methods disclosed herein are used toanalyze fetal cells from maternal serum or plasma, wherein the cellpopulation is mixed and comprises fetal cells and maternal cells. Insome aspects, the system and methods of the present disclosure are usedto identify embryonic or fetal cells in a mixed cell population. In someembodiments, the system and methods of the present disclosure are usedto identify embryonic or fetal cells in a mixed cell population, whereinnuclear size and shape are used to identify embryonic or fetal cells ina mixed population. In some embodiments, the systems and methodsdisclosed herein are used to sort fetal cells from a cell population.

In some embodiments, the system and methods disclosed herein are used tomeasure the count of fetal nucleated red blood cells (RBCs), wherein anincrease in fetal nucleated RBC count (or proportion) indicates thepresence of fetal aneuploidy. In some examples, a control sample (e.g.,a known blood or plasma sample from a non-pregnant individual) may beused for comparison. In some cases, the system and methods disclosedherein are used to provide a likelihood (i.e., probability) of apresence of an abnormal condition in a fetus.

In some embodiments, the system and methods disclosed herein are used toimage cells from maternal serum or plasma acquired from a pregnantfemale subject. In some aspects, the cells are not labelled. In someaspects, the cells are in a flow. In some aspects, the cells are imagedfrom different angles. In some aspects, the cells are live cells. Insome aspects, the cells are housed in a flow channel within the systemof the present disclosure, wherein the flow channel has walls formed tospace the plurality of cells within a single streamline. In someaspects, the cells are housed in a flow channel within the system of thepresent disclosure, wherein the flow channel has walls formed to rotatethe plurality of the cells within a single streamline.

In some embodiments, the system and methods disclosed herein are used toimage cells from maternal serum or plasma acquired from a pregnantfemale subject. In some aspects, a plurality of images of the cells iscollected using the system and methods of the present disclosure. Insome aspects, the plurality of images is analyzed to determine ifspecific disease conditions are present in the subject, wherein thecells are in a flow during the imaging and wherein the plurality ofimages comprises images of the cells from a plurality of angles. In someaspects, subject is the fetus. In some aspects, subject is pregnantfemale subject.

Sperm Analysis

In some embodiments, the sample used in the methods and systemsdescribed herein is a semen sample, and the system and methods of thepresent disclosure are used to identify sperm quality and/or gender. Inthese embodiments, the methods described herein comprise imaging thesemen sample from the subject according to the methods described hereinand analyzing the sperms in the semen sample for one or more features.In some embodiments, the systems and methods described herein are usedto obtain a sperm count. In some aspects, the systems and methodsdescribed herein are used to obtain information about sperm viabilityand/or health. In some aspects, the systems and methods described hereinare used to obtain information about sperm gender. In some embodiments,the sorting systems and methods described herein are used for andautomated enrichment of sperms with desired morphological features. Insome embodiment, the enriched sperms obtained according to the methodsand systems described herein are used for in-vitro fertilization. Insome aspects, the features are associated with health, motility, and/orgender.

Cancer Cells

Many cancers are diagnosed in later stages of the disease because of lowsensitivity of existing diagnostic procedures and processes. More than1.5 million people are diagnosed with cancer every year in the USA, ofwhich 600,000 people die (Jemal et al. 2010). Currently, the firstcancer screening procedure involves the detection of a tumor. Manycancer tumors, such as breast cancer are detected by self- or clinicalexamination. However, these tumors are typically detected only after thetumor reach a volume of 1 mL or 1 cc, when it contains approximately 10⁹cells. Routine screening by mammography is more sensitive and allowsdetection of a tumor before it becomes palpable, but only after theyreach an inch in diameter. MRI, positron emission tomography (PET) andsingle-photon emission computed tomography (SPECT) can reveal evensmaller tumors than can be detected by mammograms. However, theseimaging methods present significant disadvantages. Contrast agents formagnetic resonance imaging (MRI) are toxic and radionuclides deliveredfor SPECT or PET examination are sources of ionizing radiation. Becauseof its relatively poor resolution, ovarian cancer often requires severalfollow up scans with computed tomography (CT) or MRI, while undertakingall precautions to protect possible pregnancies, to reveal fine anatomyof developing tumors (Shin et al. 2011). Additionally, all of thesediagnostic techniques require dedicated facilities, expensive equipment,well trained staff, and financial coverages.

Cancer is commonly diagnosed in patients by obtaining a sample of thesuspect tissue and examining the tissue under a microscope for thepresence of malignant cells. While this process is relativelystraightforward when the anatomic location of the suspect tissue isknown, it can become quite challenging when there is no readilyidentifiable tumor or pre-cancerous lesion. For example, to detect thepresence of lung cancer from a sputum sample requires one or morerelatively rare cancer cells to be present in the sample. Therefore,patients having lung cancer may not be diagnosed properly if the sampledoes not perceptively and accurately reflect the conditions of the lung.

Conventional light microscopy, which utilizes cells mounted on glassslides, can only approximate 2D and 3D measurements because oflimitations in focal plane depth, sampling angles, and problems withcell preparations that typically cause cells to overlap in the plane ofthe image. Another drawback of light microscopy is the inherentlimitation of viewing through an objective lens where only the areawithin the narrow focal plane provides accurate data for analysis.

Flow cytometry methods generally overcome the cell overlap problem bycausing cells to flow one-by-one in a fluid stream. Unfortunately, flowcytometry systems do not generate images of cells of the same quality astraditional light microscopy, and, in any case, the images are notthree-dimensional.

In some embodiments, the system and methods disclosed herein enable theacquisition of three-dimensional imaging data of individual cells,wherein each individual cell from a cell population is imaged from aplurality of angles. In some aspects, the present disclosure is used todiagnose cancer, wherein individual cancer cells are identified,tracked, and grouped together. In some aspects, the cells are live.

In some embodiments, the system and methods disclosed herein are usedfor cancer diagnosis in a subject, the method comprising imaging a cellin a biological sample from the subject to collect a plurality of imagesof the cell and analyzing the plurality of images to determine ifcancerous cells are present in the subject, wherein the cancerous cellis in a flow during imaging and is spinning, and wherein the pluralityof images comprise images from a different spinning angles.

In some embodiments, the system and methods disclosed herein are usedfor cancer cell detection, wherein the cancerous cells are frombiological samples and are detected and tracked as they pass through thesystem of the present disclosure.

In some embodiments, the system and methods disclosed herein are used toidentify cancer cells from biological samples acquired from mammaliansubjects, wherein the cell population is analyzed by nuclear detail,nuclear contour, presence or absence of nucleoli, quality of cytoplasm,quantity of cytoplasm, nuclear aspect ratio, cytoplasmic aspect ratio,or nuclear to cytoplasmic ratio. In some aspects, the cancer cells thatare identified indicate the presence of cancer in the mammalian sample,including but not limited to, lymphoma, myeloma, neuroblastoma, breastcancer, ovarian cancer, lung cancer, rhabdomyosarcoma, small-cell lungtumors, primary brain tumors, stomach cancer, colon cancer, pancreaticcancer, urinary bladder cancer, testicular cancer, lymphomas, thyroidcancer, neuroblastoma, esophageal cancer, genitourinary tract cancer,cervical cancer, endometrial cancer, adrenal cortical cancer, orprostate cancer. In some aspects, the cancer is metastatic cancer. Insome aspects, the cancer is an early stage cancer.

In some embodiments, the system and methods disclosed herein are used toimage a large number of cells from a subject and collect a plurality ofimages of the cell, and to then classify the cells based on an analysisof one or more of the plurality of images; wherein the plurality ofimages comprise images from a plurality of cell angles and wherein thecell is tracked until the cell has been classified. In some aspects, thetracked cells are classified as cancerous. In some aspects, the subjectis a human.

In some embodiments, the cells used in the methods disclosed herein arelive cells. In some aspects, the cells that are classified as cancerouscells are isolated and subsequently cultured for potential drug compoundscreening, testing of a biologically active molecule, and/or furtherstudies.

In some embodiments, the system and methods disclosed herein are used toidentify cancer cells from a cell population from a mammalian subject.In some aspects, the subject is a human. In some aspects, the system andmethods disclosed herein are used to determine the progression of acancer, wherein samples from a subject are obtained from two differenttime points and compared using the methods of the present disclosure. Insome aspects, the system and methods disclosed herein are used todetermine the effectiveness of an anti-cancer treatment, wherein samplesfrom a subject are obtained before and after anti-cancer treatment andcomparing the two samples using the methods of the present disclosure.

In some embodiments, the system and methods disclosed herein comprise acancer detection system that uses a rapidly trained neural network,wherein the neural network detects cancerous cells by analyzing rawimages of the cell and provides imaging information from the pixels ofthe images to a neural network. In some aspects, the neural networkperforms recognition and identification of cancerous cells usinginformation derived from an image of the cells, among others, the area,the average intensity, the shape, the texture, and the DNA (pgDNA) ofthe cells. In some aspects, the neural network performs recognition ofcancerous cells using textural information derived from an image of thecells, among them angular second moment, contrast, coefficient ofcorrelation, sum of squares, difference moment, inverse differencemoment, sum average, sum variance, sum entropy, entry, differencevariance, difference entropy, information measures, maximal correlationcoefficient, coefficient of variation, peak transition probability,diagonal variance, diagonal moment, second diagonal moment, productmoment, triangular symmetry and blobness.

Bacteria from Human Cells

In some embodiments, the methods disclosed herein are used for bacterialdetection, wherein the human cells containing bacteria are frombiological samples and are detected and tracked as they pass through thesystem of the present disclosure.

In some embodiments, the system and methods disclosed herein enable theacquisition of three-dimensional imaging data of bacteria present in asample, wherein each individual bacterium is imaged from a plurality ofangles. In some embodiments, the system and methods disclosed herein areused for bacterial detection, wherein the bacteria is from biologicalsamples and are detected and tracked as they pass through the system ofthe present disclosure.

In some embodiments, the system and methods disclosed herein are used todetect bacteria in fluids, including blood, platelets, and other bloodproducts for transfusion, and urine. In some aspects, the presentdisclosure provides a method for separating intact eukaryotic cells fromsuspected intact bacterial cells that may be present in the fluidsample. In some aspects, the present disclosure identifies certainbacterial species, including but not limited to: Bacillus cereus,Bacillus subtilis, Clostridium perfringens, Corynebacterium species,Escherichia coli, Enterobacter cloacae, Klebsiella oxytoca,Propionibacterium acnes, Pseudomonas aeruginosa, Salmonellacholeraesuis, Serratia marcesens, Staphylococcus aureus, Staphylococcusepidermidis, Streptococcus pyogenes, and Streptococcus viridans.

In some embodiments, the system and methods disclosed herein comprise abacterial detection system that uses a rapidly trained neural network,wherein the neural network detects bacteria by analyzing raw images ofthe cell and provides imaging information from the pixels of the imagesto a neural network. In some aspects, the neural network performsrecognition and identification of bacteria using information derivedfrom an image of the bacteria, among others, the area, the averageintensity, the shape, the texture, and the DNA (pgDNA) of the cells. Insome aspects, the neural network performs recognition of cancerous cellsusing textural information derived from an image of the cells, amongthem angular second moment, contrast, coefficient of correlation, sum ofsquares, difference moment, inverse difference moment, sum average, sumvariance, sum entropy, entry, difference variance, difference entropy,information measures, maximal correlation coefficient, coefficient ofvariation, peak transition probability, diagonal variance, diagonalmoment, second diagonal moment, product moment, triangular symmetry andblobness.

Sepsis

In some embodiments, the system and methods disclosed herein are usedfor the detection and/or identification of sepsis. Without wishing to bebound by theory, plasma cells (e.g., myeloid cells such as white bloodcells, lymphocytes, etc.) of a subject with hematologic bacterialinfections, such as sepsis, may exhibit different morphological features(e.g., geometry, texture, shape, aspect ratio, area, etc.) than those ofa subject without the hematologic bacterial infection. Thus, in someexamples, the classification and sorting processes, as provided herein,may be used to diagnosis sepsis.

Sickle Cell Disease

In some embodiments, the system and methods disclosed herein are usedfor the detection and/or identification of a sickle cell. In someaspects, the system and methods disclosed herein are used to image acell and to determine if the cell is a sickle cell. The methods of thedisclosure may be further used to collect the cells determined to besickle cells. In some embodiments the cell is from a biological samplefrom a subject and the methods disclosed herein are used to determinewhether the subject suffers from or is susceptible to a sickle celldisease. In some embodiments, the sickle cell disease is a sickle cellanemia.

Crystals in Biological Samples

Current diagnostic methods used to detect crystals in blood and/or urineincludes radiological, serological, sonographic, and enzymatic methods.

Urine crystals may be of several different types. Most commonly crystalsare formed of struvite (magnesium-ammonium-phosphate), oxalate, urate,cysteine, or silicate, but may also be composed of other materials suchas bilirubin, calcium carbonate, or calcium phosphate.

In some embodiments, the system and methods disclosed herein are usedfor the detection of crystals in biological samples. In some aspects,detected crystals are formed. In some aspects, the biological samplefrom a subject is imaged according to the methods described herein todetermine whether the biological sample comprises a crystal. In someaspects, the biological sample is blood. In some aspects, the blood isvenous blood of a subject. In some aspects, the biological sample isurine. In some aspects, the subject is a human, horse, rabbit, guineapig, or goat. In some aspects, the methods of the disclosure may befurther utilized to isolate and collect the crystal from the sample. Insome aspects, the biological sample is from a subject and the system andmethods of the present disclosure are used to determine whether thesubject suffers from or is susceptible to disease or a condition.

In some embodiments, the methods disclosed herein are used for theanalysis of a crystal from a biological sample. In some aspects, themethods disclosed herein may be used to image a crystal, and the crystalimages may be analyzed for, including but not limited to, crystal shape,size, texture, morphology, and color. In some embodiments, thebiological sample is from a subject and the methods disclosed herein areused to determine whether the subject suffers from a disease or acondition. In some example the subject is a human. For example, themethods of the disclosure may be used to analyze crystal in a bloodsample of the human subject, and the results may be used to determinewhether the subject suffers from pathological conditions, including butnot limited to, chronic or rheumatic leukemia. In some aspects, thebiological sample is a urine sample.

In some embodiments, the system and methods disclosed herein enable theacquisition of three-dimensional imaging data of crystals, if found inthe biological sample, wherein each individual crystal is imaged from aplurality of angles.

In some embodiments, the system and methods disclosed herein comprise acrystal detection system that uses a rapidly trained neural network,wherein the neural network detects crystals by analyzing raw images of aplurality of crystals and provides imaging information from the pixelsof the images to a neural network. In some aspects, the neural networkperforms recognition and identification of a plurality of crystals usinginformation derived from an image of the crystals, among others, thearea, the average intensity, the shape, the texture. In some aspects,the neural network performs recognition of crystals using texturalinformation derived from an image of the cells, among them angularsecond moment, contrast, coefficient of correlation, sum of squares,difference moment, inverse difference moment, sum average, sum variance,sum entropy, entry, difference variance, difference entropy, informationmeasures, maximal correlation coefficient, coefficient of variation,peak transition probability, diagonal variance, diagonal moment, seconddiagonal moment, product moment, triangular symmetry and blobness.

Liquid Biopsy

A liquid biopsy comprises the collection of blood and/or urine from acancer patient with primary or recurrent disease and the analysis ofcancer-associated biomarkers in the blood and/or urine. A liquid biopsyis a simple and non-invasive alternative to surgical biopsies thatenables doctors to discover a range of information about a tumor. Liquidbiopsies are increasingly being recognized as a viable, noninvasivemethod of monitoring a patient's disease progression, regression,recurrence, and/or response to treatment.

In some embodiments, the methods disclosed herein are used for liquidbiopsy diagnostics, wherein the biopsy is a liquid biological samplethat is passed through the system of the present disclosure. In someaspects, the liquid biological sample that is used for the liquid biopsyis less than 5 mL of liquid. In some aspects, the liquid biologicalsample that is used for the liquid biopsy is less than 4 mL of liquid.In some aspects, the liquid biological sample that is used for theliquid biopsy is less than 3 mL of liquid. In some aspects, the liquidbiological sample that is used for the liquid biopsy is less than 2 mLof liquid. In some aspects, the liquid biological sample that is usedfor the liquid biopsy is less than 1 mL of liquid. In some aspects, theliquid biological sample that is used for liquid biopsy is centrifugedto get plasma.

In some embodiments, the system and methods of the present disclosureare used for body fluid sample assessment, wherein cells within a sampleare imaged and analyzed and a report is generated comprising all thecomponents within the sample, the existence of abnormalities in thesample, and a comparison to previously imaged or tested samples from thesame patient or the baseline of other healthy individuals.

In some embodiments, the system and methods of the present disclosureare used for the diagnosis of immune diseases, including but not limitedto tuberculosis (TB) and acquired immune deficiency disorder (AIDS),wherein white blood cells are imaged in the system disclosed herein toexamine their capacity to release pro- and anti-inflammatory cytokines.

In some embodiments, the system and methods of the present disclosureare used to assess patient immune responses to immunomodulatorytherapies by imaging their white blood cells and analyzing the change intheir capacity to release pro- and anti-inflammatory cytokines.

In some embodiments, the system and methods of the present disclosureare used to identify the efficacy of therapeutics and/or to guide theselection of agents or their dosage by isolating patients' white bloodcells and analyzing the effect of target therapeutics on their capacityto release pro- and anti-inflammatory cytokines.

In some embodiments, the system and methods of the present disclosureare used to isolate pure samples of stem cell-derived tissue cells byobtaining images of cells, and isolating cells with desired phenotype.

Testing Biologically Active Molecules

In some embodiments, the methods disclosed herein are used forbiologically active molecule testing, for example drugs. In someembodiments, the methods of the disclosure are sued to collect desiredcells from a sample and then treating the desired cells with abiologically active molecule in order to test the effect of thebiologically active molecule on the collected cells.

In some embodiments, the methods and systems of the present disclosureare used for identifying the efficacy of therapeutics. In some aspects,identifying the efficacy of therapeutics using the system disclosedherein is carried out by obtaining images of a cell before and aftertreatment and analyzing the images to determine whether the cell hasresponded to the therapeutic of interest.

In some embodiments, the system and methods disclosed herein are usedfor diseased cell detection, wherein the diseased cells are frombiological samples and are detected and tracked as they pass through thesystem of the present disclosure. In some aspects, the diseased cellsare isolated and grouped together for further studies.

In some embodiments, the cells used in the methods disclosed herein arelive cells. In some aspects, the cells that are classified as diseasedcells are isolated and subsequently cultured for potential drug compoundscreening, testing of a biologically active molecule, and/or furtherstudies.

Although the present disclosure has been described in certain specificaspects, many additional modifications and variations would be apparentto those skilled in the art. It is therefore to be understood that thepresent disclosure can be practiced otherwise than specificallydescribed without departing from the scope and spirit of the presentdisclosure. Thus, some embodiments of the present disclosure should beconsidered in all respects as illustrative and not restrictive.

Point-of-Care Complete Blood Count (CBC)

CBC may provide information about types and numbers of cells in blood orplasma. White blood cell (WBC) count may be used as biomarkers for acuteinfection and/or inflammation. While an elevated WBC may be associatedwith infection, inflammation, tissue injury, leukemia and allergy, a lowWBC count may be associated with viral infections, immunodeficiency,acute leukemia and bone marrow failure. Thus, an efficient point-of-careCBC may enhance (e.g., expedite) any clinical decision process thatrequires such information. Thus, a facility (e.g., a hospital, pharmacy,any point-of-care site, etc.) may comprise any subject embodiment of theflow cell of the present disclosure to analyze a subject's blood (orplasma) and obtain the CBC. Furthermore, the flow cell provided hereinmay provide CBC to track the number of WBCs before and after eachtreatment for a subject (e.g., chemotherapy treatment for a cancerpatient). As such, in some cases, the flow cell provided herein maynegate a need for hematological analysis-based CBC, which is oftenperformed in a central or satellite laboratories.

Computer Systems

The present disclosure provides computer systems that are programmed toimplement methods of the disclosure. FIG. 16 shows a computer system1601 that is programmed or otherwise configured to capture and/oranalyze one or more images of the cell. The computer system 1601 canregulate various aspects of components of the cell sorting system of thepresent disclosure, such as, for example, the pump, the valve, and theimaging device. The computer system 1601 can be an electronic device ofa user or a computer system that is remotely located with respect to theelectronic device. The electronic device can be a mobile electronicdevice.

The computer system 1601 includes a central processing unit (CPU, also“processor” and “computer processor” herein) 1605, which can be a singlecore or multi core processor, or a plurality of processors for parallelprocessing. The computer system 1601 also includes memory or memorylocation 1610 (e.g., random-access memory, read-only memory, flashmemory), electronic storage unit 1615 (e.g., hard disk), communicationinterface 1620 (e.g., network adapter) for communicating with one ormore other systems, and peripheral devices 1625, such as cache, othermemory, data storage and/or electronic display adapters. The memory1610, storage unit 1615, interface 1620 and peripheral devices 1625 arein communication with the CPU 1605 through a communication bus (solidlines), such as a motherboard. The storage unit 1615 can be a datastorage unit (or data repository) for storing data. The computer system1601 can be operatively coupled to a computer network (“network”) 1630with the aid of the communication interface 1620. The network 1630 canbe the Internet, an internet and/or extranet, or an intranet and/orextranet that is in communication with the Internet. The network 1630 insome cases is a telecommunication and/or data network. The network 1630can include one or more computer servers, which can enable distributedcomputing, such as cloud computing. The network 1630, in some cases withthe aid of the computer system 1601, can implement a peer-to-peernetwork, which may enable devices coupled to the computer system 1601 tobehave as a client or a server.

The CPU 1605 can execute a sequence of machine-readable instructions,which can be embodied in a program or software. The instructions may bestored in a memory location, such as the memory 1610. The instructionscan be directed to the CPU 1605, which can subsequently program orotherwise configure the CPU 1605 to implement methods of the presentdisclosure. Examples of operations performed by the CPU 1605 can includefetch, decode, execute, and writeback.

The CPU 1605 can be part of a circuit, such as an integrated circuit.One or more other components of the system 1601 can be included in thecircuit. In some cases, the circuit is an application specificintegrated circuit (ASIC).

The storage unit 1615 can store files, such as drivers, libraries andsaved programs. The storage unit 1615 can store user data, e.g., userpreferences and user programs. The computer system 1601 in some casescan include one or more additional data storage units that are externalto the computer system 1601, such as located on a remote server that isin communication with the computer system 1601 through an intranet orthe Internet.

The computer system 1601 can communicate with one or more remotecomputer systems through the network 1630. For instance, the computersystem 1601 can communicate with a remote computer system of a user.Examples of remote computer systems include personal computers (e.g.,portable PC), slate or tablet PC's (e.g., Apple® iPad, Samsung® GalaxyTab), telephones, Smart phones (e.g., Apple® iPhone, Android-enableddevice, Blackberry®), or personal digital assistants. The user canaccess the computer system 1601 via the network 1630.

Methods as described herein can be implemented by way of machine (e.g.,computer processor) executable code stored on an electronic storagelocation of the computer system 1601, such as, for example, on thememory 1610 or electronic storage unit 1615. The machine executable ormachine readable code can be provided in the form of software. Duringuse, the code can be executed by the processor 1605. In some cases, thecode can be retrieved from the storage unit 1615 and stored on thememory 1610 for ready access by the processor 1605. In some situations,the electronic storage unit 1615 can be precluded, andmachine-executable instructions are stored on memory 1610.

The code can be pre-compiled and configured for use with a machinehaving a processer adapted to execute the code, or can be compiledduring runtime. The code can be supplied in a programming language thatcan be selected to enable the code to execute in a pre-compiled oras-compiled fashion.

Aspects of the systems and methods provided herein, such as the computersystem 1601, can be embodied in programming. Various aspects of thetechnology may be thought of as “products” or “articles of manufacture”typically in the form of machine (or processor) executable code and/orassociated data that is carried on or embodied in a type of machinereadable medium. Machine-executable code can be stored on an electronicstorage unit, such as memory (e.g., read-only memory, random-accessmemory, flash memory) or a hard disk. “Storage” type media can includeany or all of the tangible memory of the computers, processors or thelike, or associated modules thereof, such as various semiconductormemories, tape drives, disk drives and the like, which may providenon-transitory storage at any time for the software programming. All orportions of the software may at times be communicated through theInternet or various other telecommunication networks. Suchcommunications, for example, may enable loading of the software from onecomputer or processor into another, for example, from a managementserver or host computer into the computer platform of an applicationserver. Thus, another type of media that may bear the software elementsincludes optical, electrical and electromagnetic waves, such as usedacross physical interfaces between local devices, through wired andoptical landline networks and over various air-links. The physicalelements that carry such waves, such as wired or wireless links, opticallinks or the like, also may be considered as media bearing the software.As used herein, unless restricted to non-transitory, tangible “storage”media, terms such as computer or machine “readable medium” refer to anymedium that participates in providing instructions to a processor forexecution.

Hence, a machine readable medium, such as computer-executable code, maytake many forms, including but not limited to, a tangible storagemedium, a carrier wave medium or physical transmission medium.Non-volatile storage media include, for example, optical or magneticdisks, such as any of the storage devices in any computer(s) or thelike, such as may be used to implement the databases, etc. shown in thedrawings. Volatile storage media include dynamic memory, such as mainmemory of such a computer platform. Tangible transmission media includecoaxial cables; copper wire and fiber optics, including the wires thatcomprise a bus within a computer system. Carrier-wave transmission mediamay take the form of electric or electromagnetic signals, or acoustic orlight waves such as those generated during radio frequency (RF) andinfrared (IR) data communications. Common forms of computer-readablemedia therefore include for example: a floppy disk, a flexible disk,hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD orDVD-ROM, any other optical medium, punch cards paper tape, any otherphysical storage medium with patterns of holes, a RAM, a ROM, a PROM andEPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wavetransporting data or instructions, cables or links transporting such acarrier wave, or any other medium from which a computer may readprogramming code and/or data. Many of these forms of computer readablemedia may be involved in carrying one or more sequences of one or moreinstructions to a processor for execution.

The computer system 1601 can include or be in communication with anelectronic display 1635 that comprises a user interface (UI) 1640 forproviding, for example, the one or more images of the cell that istransported through the channel of the cell sorting system. In somecases, the computer system 1601 can be configured to provide a livefeedback of the images. Examples of UI's include, without limitation, agraphical user interface (GUI) and web-based user interface.

Methods and systems of the present disclosure can be implemented by wayof one or more algorithms. An algorithm can be implemented by way ofsoftware upon execution by the central processing unit 1605. Thealgorithm can be, for example, a deep learning algorithm to enablesorting of the cell.

EXAMPLES

The following specific examples are illustrative and non-limiting. Theexamples described herein reference and provide non-limiting support tothe various embodiments described in the preceding sections.

Example 1. Non-Invasive Prenatal Testing

Sample Preparation: Five to ten mL of maternal peripheral blood will becollected into an ethylene diamine tetraacetic acid (EDTA)-containingtube and a plain tube. For women undergoing amniocentesis, maternalblood will be collected prior to the procedure. Maternal blood sampleswill be processed between 1 to 3 hours following venesection. Bloodsamples will be centrifuged at 3000 g and plasma and serum will becarefully removed from the EDTA-containing and plain tubes,respectively, and transferred into plain polypropylene tubes. The plasmaor serum samples must remain undisturbed when the buffy coat or theblood clot are removed. Following removal of the plasma samples, the redcell pellet and buffy coat will be saved for processing in the system ofthe present disclosure.

Sample Testing: Cells from maternal serum or plasma acquired from apregnant female subject may be imaged using the system and methods ofthe present disclosure, wherein the cells are not labelled and placed ina flow. The cells will be imaged from different angles. In some aspects,the cells will be housed in a flow channel within the system of thepresent disclosure, wherein the flow channel has walls formed to spacethe plurality of cells within a single streamline. In some aspects, thecells will be housed in a flow channel within the system of the presentdisclosure, wherein the flow channel has walls formed to rotate theplurality of the cells within a single streamline.

While preferred embodiments of the present invention have been shown anddescribed herein, it will be obvious to those skilled in the art thatsuch embodiments are provided by way of example only. It is not intendedthat the invention be limited by the specific examples provided withinthe specification. While the invention has been described with referenceto the aforementioned specification, the descriptions and illustrationsof the embodiments herein are not meant to be construed in a limitingsense. Numerous variations, changes, and substitutions will now occur tothose skilled in the art without departing from the invention.Furthermore, it shall be understood that all aspects of the inventionare not limited to the specific depictions, configurations or relativeproportions set forth herein which depend upon a variety of conditionsand variables. It should be understood that various alternatives to theembodiments of the invention described herein may be employed inpracticing the invention. It is therefore contemplated that theinvention shall also cover any such alternatives, modifications,variations or equivalents. It is intended that the following claimsdefine the scope of the invention and that methods and structures withinthe scope of these claims and their equivalents be covered thereby.

1.-119. (canceled)
 120. A cell flow system comprising: a flow channelconfigured to transport a cell through the flow channel; a plurality ofsub-channels in fluid communication with the flow channel via a sortingjunction; at least one sensor operatively coupled to the sortingjunction, wherein the at least one sensor is configured to detect anactual arrival time of the cell at the sorting junction; and a processorconfigured to: (1) analyze (i) the actual arrival time of the cell atthe sorting junction and (ii) a predicted arrival time of the cell atthe sorting junction; and (2) control an operation of the cell flowsystem based on the analysis in (1).
 121. The cell flow system of claim120, wherein the processor is configured to compare (i) the actualarrival time and (ii) the predicted arrival time.
 122. The cell flowsystem of claim 120, wherein the predicted arrival time is obtainedprior to the detection of the actual arrival time.
 123. The cell flowsystem of claim 120, wherein the predicted arrival time comprises aprobability of the cell's arrival at the sorting junction.
 124. The cellflow system of claim 120, wherein the processor is configured todetermine the predicted arrival time of the cell by a deep learningalgorithm.
 125. The cell flow system of claim 124, wherein the processoris further configured to update the deep learning algorithm based on theanalysis in (1).
 126. The cell flow system of claim 125, wherein thedeep learning algorithm is updated in real time.
 127. The cell flowsystem of claim 120, wherein the at least one sensor is disposedadjacent to the sorting junction.
 128. The cell flow system of claim120, wherein the at least one sensor is further configured to obtaindata indicative of a velocity of the cell at a position along the flowchannel and upstream of the sorting junction.
 129. The cell flow systemof claim 128, wherein the at least one sensor comprises an opticaldevice.
 130. The cell flow system of claim 129, wherein the opticaldevice comprises one or more cameras configured to capture one or moreimages of the cell.
 131. The cell flow system of claim 130, wherein theone or more images comprise a two-dimensional image.
 132. The cell flowsystem of claim 130, wherein the one or more images comprise athree-dimensional image.
 133. The cell flow system of claim 19, whereinthe optical device comprises one or more laser detectors.
 134. The cellflow system of claim 133, wherein the one or more laser detectorscomprise a multi-point laser detector.
 135. The cell flow system ofclaim 128, wherein the processor is configured to determine thepredicted arrival time based on (i) the velocity of the cell and (ii) adistance between the position and the sorting junction.
 136. The cellflow system of claim 120, wherein the operation comprises one or moremembers selected from the group consisting of (i) focusing a pluralityof cells into a streamline within the flow channel, (ii) rotating theplurality of cells within the flow channel, (iii) adjusting a velocityof the plurality of cells within the flow channel, and (iv) one or morecell classification algorithms.
 137. The cell flow system of claim 120,wherein the operation comprises two or more members selected from thegroup consisting of (i)-(iv).
 138. The cell flow system of claim 120,wherein the cell is from a biological sample of a subject.
 139. The cellflow system of claim 138, wherein the biological sample comprises bloodor serum.